Detection of genetic or molecular aberrations associated with cancer

ABSTRACT

Biological samples including cell-free DNA fragments are analyzed to identify imbalances in chromosomal regions, e.g., due to deletions and/or amplifications in a tumor. Multiple loci are used for each chromosomal region. Such imbalances can then be used to diagnose (screen) a patient for cancer, as well as prognosticate a patient with cancer, or to detect the presence or to monitor the progress of a premalignant condition in a patient. The severity of an imbalance as well as the number of regions exhibiting an imbalance can be used. A systematic analysis of non-overlapping segments of a genome can provide a general screening tool for a sample. Additionally, a patient can be tested over time to track severity of each of one or more chromosomal regions and a number of chromosomal regions to enable screening and prognosticating, as well as monitoring of progress (e.g. after treatment).

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority from and is a non provisionalapplication of U.S. Provisional Application No. 61/418,391, entitled“DETECTION OF GENETIC ABERRATIONS ASSOCIATED WITH CANCER” filed Nov. 30,2010, and U.S. Provisional Application No. 61/529,877, entitled“DETECTION OF GENETIC OR MOLECULAR ABERRATIONS ASSOCIATED WITH CANCER”filed Aug. 31, 2011, the entire contents of which are hereinincorporated by reference for all purposes.

This application is related to commonly owned U.S. patent applicationSer. No. 12/940,992 (U.S. Publication 2011/0276277) entitled “Size-BasedGenomic Analysis” by Lo et al., filed Nov. 5, 2010, and U.S. patentapplication Ser. No. 12/940,993 (U.S. Publication 2011/0105353) entitled“Fetal Genomic Analysis From A Maternal Biological Sample” by Lo et al.,filed Nov. 5, 2010, the disclosures of which is incorporated byreference in its entirety.

BACKGROUND

Cancer is a common disease that affects many people. Often cancer is notidentified until severe symptoms manifest. For common types of cancer,there are screening techniques to identify patients who may have cancer.But, such techniques are often unreliable or subject a patient toradiation. For many other types of cancers, there are no effectivescreening techniques.

Loss of heterozygosity (LOH) has been detected for a particular locus inthe circulating DNA of patients suffering from lung, and head and neckcancers (Chen X Q, et al. Nat Med 1996; 2: 1033-5; Nawroz H, et al. NatMed 1996; 2: 1035-7). However, such techniques have been hindered by arelative small amount of LOH that has been detectable from examining aparticular locus. Even when using digital PCR, these methods stillsuffer from an inability to detect small amounts of LOH. Moreover, suchtechniques have still been limited to investigating a particular locusthat is known to occur in a specific type of cancer. Thus, a screeningfor cancer in general has not been possible or effective.

Besides screening for an existence of cancer, current techniques arealso lacking for providing a prognosis of a patient with cancer and formonitoring the effects of treatment (e.g. recovery after surgery orchemotherapy or immunotherapy or targeted therapy). Such techniques areoften expensive (e.g. imaging techniques), inaccurate, ineffective,insensitive or may subject the patient to the radiation used for imagingtechniques.

Accordingly, it is desirable to provide new techniques for screening,prognosticating, and monitoring a patient for cancer.

BRIEF SUMMARY

Embodiments provide systems, apparatus, and methods for determininggenetic aberrations associated with cancer. Biological samples includingcell-free DNA fragments are analyzed to identify imbalances inchromosomal regions, e.g., due to deletions and/or amplifications in atumor. Using a chromosomal region with multiple loci can allow forgreater efficiency and/or accuracy. Such imbalances can then be used todiagnose or screen a patient for cancer, as well as prognosticate apatient with cancer. The severity of an imbalance as well as the numberof regions exhibiting an imbalance can be used. Additionally, a patientcan be tested over time to track severity of each of one or morechromosomal regions and a number of chromosomal regions to enablescreening and prognosticating, as well as monitoring of progress (e.g.after treatment).

According to one embodiment, a method of analyzing a biological sampleof an organism for chromosomal deletions or amplifications associatedwith cancer is provided. The biological sample includes nucleic acidmolecules originating from normal cells and potentially from cellsassociated with cancer. At least some of the nucleic acid molecules arecell-free in the sample. First and second haplotypes are determined fornormal cells of the organism at a first chromosomal region. The firstchromosomal region including a first plurality of heterozygous loci.Each of a plurality of the nucleic acid molecules in the sample have alocation in a reference genome of the organism identified and have arespective allele determined. The locations and determined alleles areused to determine a first group of nucleic acid molecules from the firsthaplotype and a second group from the second haplotype. A computersystem calculates a first value of the first group and a second value ofthe second group. Each value defined a property of the respective groupof nucleic acid molecules (e.g. an average size or number of moleculesin the group). The first value is compared to the second value todetermine a classification of whether the first chromosomal regionexhibits a deletion or an amplification in any cells associated withcancer.

According to another embodiment, a method of analyzing a biologicalsample of an organism is provided. The biological sample includesnucleic acid molecules originating from normal cells and potentiallyfrom cells associated with cancer. At least some of the nucleic acidmolecules are cell-free in the sample. A plurality of non-overlappingchromosomal regions of the organism are identified. Each chromosomalregion includes a plurality of loci. Each of a plurality of the nucleicacid molecules in the sample have a location in a reference genome ofthe organism identified. For each chromosomal region, a respective groupof nucleic acid molecules are identified as being from the chromosomalregion based on the identified locations. Each respective group includesat least one nucleic acid molecule located at each of the plurality ofloci of the chromosomal region. A computer system calculates arespective value of the respective group, where the respective valuedefines a property of the nucleic acid molecules of the respectivegroup. The respective value is compared to a reference value todetermine a classification of whether the chromosomal region exhibits adeletion or an amplification. An amount of chromosomal regionsclassified as exhibiting a deletion or amplification is then determined.

According to another embodiment, a method is provided for determining aprogress of chromosomal aberrations in an organism using biologicalsamples including nucleic acid molecules originating from normal cellsand potentially from cells associated with cancer. At least some of thenucleic acid molecules are cell-free in the biological samples. One ormore non-overlapping chromosomal regions are identified for a referencegenome of the organism. Each chromosomal region includes a plurality ofloci. Samples taken from the organism at different times are analyzed todetermine the progress. For a sample, each of a plurality of the nucleicacid molecules in the sample have a location in a reference genome ofthe organism identified. For each chromosomal region, a respective groupof nucleic acid molecules are identified as being from the chromosomalregion based on the identified locations. The respective group includingat least one nucleic acid molecule located at each of the plurality ofloci of the chromosomal region. A computer system calculates arespective value of the respective group of nucleic acid molecules. Therespective value defines a property of the nucleic acid molecules of therespective group. The respective value is compared to a reference valueto determine a classification of whether the first chromosomal regionexhibits a deletion or an amplification. Then, the classifications ofeach of the chromosomal regions at the plurality of times are used todetermine the progress of the chromosomal aberrations in the organism.

Other embodiments of the invention are directed to systems, portableconsumer devices, and computer readable media associated with methodsdescribed herein.

A better understanding of the nature and advantages of the presentinvention may be gained with reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a chromosomal region of a cancer cell exhibiting anaberration of a deletion.

FIG. 2 illustrates a chromosomal region of a cancer cell exhibiting anaberration of an amplification.

FIG. 3 shows a table 300 illustrating different types of cancers andassociated regions and their corresponding aberrations.

FIG. 4 illustrates chromosomal regions within a cancer cell that do notexhibit an aberration along with measurements made in plasma accordingto embodiments of the present invention.

FIG. 5 illustrates the deletion of chromosomal region 510 within acancer cell along with measurements made in plasma to determine thedeleted region according to embodiments of the present invention.

FIG. 6 illustrates the amplification of chromosomal region 610 within acancer cell along with measurements made in plasma to determine theamplified region according to embodiments of the present invention.

FIG. 7 shows an RHDO analysis of the plasma DNA of an hepatocellularcarcinoma (HCC) patient for a segment located at chromosome 1p whichshowed mono-allelic amplification in the tumor tissue according toembodiments of the present invention.

FIG. 8 shows the change in size distribution of fragments for twohaplotypes of a chromosomal region when a tumor containing a deletion ispresent according to embodiments of the present invention.

FIG. 9 shows the change in size distribution of fragments for twohaplotypes of a chromosomal region when a tumor containing anamplification is present according to embodiments of the presentinvention.

FIG. 10 is a flowchart illustrating a method of analyzing the haplotypesof a biological sample of an organism to determine whether a chromosomalregion exhibits a deletion or an amplification according to embodimentsof the present invention.

FIG. 11 shows a region 1110 with a subregion 1130 being deleted incancer cells along with measurements made in plasma to determine thedeleted region according to embodiments of the present invention.

FIG. 12 shows how the location of the aberrations can be mapped usingRHDO analysis according to embodiments of the present invention.

FIG. 13 shows an RHDO classification started from another directionaccording to embodiments of the present invention.

FIG. 14 is a flowchart of a method 1400 for analyzing a biologicalsample of an organism using a plurality of chromosomal regions accordingto embodiments of the present invention.

FIG. 15 shows a table 1500 illustrating the depth required for variousnumbers of segments and fractional concentration of tumor-derivedfragments according to embodiments of the present invention. FIG. 15provides an estimation of the number of molecules to be analyzed fordifferent percentage of fractional concentration of cancer-derived DNAin a sample.

FIG. 16 shows a principle of measuring the fractional concentration oftumor-derived DNA in plasma by relative haplotype dosage (RHDO) analysisaccording to embodiments of the present invention. Hap I and Hap IIrepresents the two haplotypes in the non-tumor tissues according toembodiments of the present invention.

FIG. 17 is a flowchart illustrating a method of determining a progressof chromosomal aberrations in an organism using biological samplesincluding nucleic acid molecules according to embodiments of the presentinvention.

FIG. 18A shows an SPRT curve for RHDO analysis for a segment on the qarm of chromosome 4 for a patient with cancer. The dots represent theratio the cumulative counts after respective heterozygous loci. FIG. 18Bshows an SPRT curve for RHDO analysis for a segment on the q arm ofchromosome 4 for the patient after treatment.

FIG. 19 shows common chromosomal aberrations found in HCC.

FIG. 20A shows the results normalized tag counts ratio for the HCC andhealthy patients using targeted analysis. FIG. 20B shows the results ofa size analysis after target enrichment and massively parallelsequencing for the 3 HCC patients and 4 healthy control subjects.

FIG. 21 shows Circos plots of a HCC patient depicting data fromsequenced tag counting of plasma DNA according to embodiments of thepresent invention.

FIG. 22 shows a sequenced tag counting analysis for the plasma sample ofa chronic hepatitis B virus (HBV) carrier without HCC according toembodiments of the present invention.

FIG. 23 shows a sequenced tag counting analysis for the plasma sample ofa patient with stage 3 nasopharyngeal carcinoma (NPC) according toembodiments of the present invention.

FIG. 24 shows a sequenced tag counting analysis for the plasma sample ofa patient with stage 4 NPC according to embodiments of the presentinvention.

FIG. 25 shows a plot of cumulative frequency of plasma DNA against sizefor a region exhibiting loss of heterozygosity (LOH) in the tumor tissueaccording to embodiments of the present invention.

FIG. 26 shows ΔQ against the size of sequenced plasma DNA for the LOHregion. ΔQ reaches 0.2 at the size of 130 bp according to embodiments ofthe present invention.

FIG. 27 shows a plot of cumulative frequency of plasma DNA against sizefor a region with chromosomal duplication in the tumor tissue accordingto embodiments of the present invention.

FIG. 28 shows ΔQ against the size of sequenced plasma DNA for theamplified region according to embodiments of the present invention.

FIG. 29 shows a block diagram of an example computer system 900 usablewith system and methods according to embodiments of the presentinvention.

DEFINITIONS

The term “biological sample” as used herein refers to any sample that istaken from a subject (e.g., a human, a person with cancer, a personsuspected of having cancer, or other organisms) and contains one or morenucleic acid molecule(s) of interest.

The term “nucleic acid” or “polynucleotide” refers to a deoxyribonucleicacid (DNA) or ribonucleic acid (RNA) and a polymer thereof in eithersingle- or double-stranded form. Unless specifically limited, the termencompasses nucleic acids containing known analogs of naturalnucleotides that have similar binding properties as the referencenucleic acid and are metabolized in a manner similar to naturallyoccurring nucleotides. Unless otherwise indicated, a particular nucleicacid sequence also implicitly encompasses conservatively modifiedvariants thereof (e.g., degenerate codon substitutions), alleles,orthologs, single nucleotide polymorphisms (SNPs), copy number variants,and complementary sequences as well as the sequence explicitlyindicated. Specifically, degenerate codon substitutions may be achievedby generating sequences in which the third position of one or moreselected (or all) codons is substituted with mixed-base and/ordeoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991);Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini etal., Mol. Cell. Probes 8:91-98 (1994)). The term nucleic acidencompasses, but is not limited to: gene, cDNA, mRNA, small noncodingRNA, micro RNA (miRNA), Piwi-interacting RNA, and short hairpin RNA(shRNA) encoded by a gene or locus.

The term “gene” means the segment of DNA involved in producing apolypeptide chain or transcribed RNA product. It may include regionspreceding and following the coding region (leader and trailer) as wellas intervening sequences (introns) between individual coding segments(exons).

The term “clinically relevant nucleic acid sequence” or “clinicallyrelevant chromosomal region” (or region/segment being tested) as usedherein can refer to a polynucleotide sequence corresponding to a segmentof a larger genomic sequence whose potential imbalance is being testedor to the larger genomic sequence itself Examples include genomicsegments that are or potentially are deleted or amplified (includingsimple duplication), or to a larger region that includes the subregionof the segment. In some embodiments, multiple clinically relevantnucleic acid sequences, or equivalently multiple markers of theclinically relevant nucleic acid sequence, can be used to provide datafor detecting an imbalance in a region. For instance, data from fivenon-consecutive sequences on a chromosome can be used in an additivefashion for the determination of a possible imbalance, effectivelyreducing the needed sample volume to 1/5.

The term “reference nucleic acid sequence” or “reference chromosomalregion” as used herein refers to a nucleic acid sequence whosequantitative profile or size distribution is used to compare against thetest region. Examples of a reference nucleic acid sequence include achromosomal region that does not include a deletion or amplification,the entire genome (e.g. via a normalization by total sequenced tagcounts), a region from one or more samples known to be normal (whichcould be the same region for the sample being tested), or a particularhaplotype of a chromosomal region. Such reference nucleic acid sequencescan either exist endogenously in the sample, or added exogenously duringsample processing or analysis. In some embodiments, the referencechromosomal region demonstrates a size profile that is representative ofa healthy state without disease. In yet other embodiments, the referencechromosomal region demonstrates a quantitative profile that isrepresentative of a healthy state without disease.

The term “based on” as used herein means “based at least in part on” andrefers to one value (or result) being used in the determination ofanother value, such as occurs in the relationship of an input of amethod and the output of that method. The term “derive” as used hereinalso refers to the relationship of an input of a method and the outputof that method, such as occurs when the derivation is the calculation ofa formula.

The term “parameter” as used herein means a numerical value thatcharacterizes a quantitative data set and/or a numerical relationshipbetween quantitative data sets. For example, a ratio (or function of aratio) between a first amount of a first nucleic acid sequence and asecond amount of a second nucleic acid sequence is a parameter.

As used herein, the term “locus” or its plural form “loci” is a locationor address of any length of nucleotides (or base pairs) which may have avariation across genomes.

The term “sequence imbalance” or “aberration” as used herein means anysignificant deviation as defined by at least one cutoff value in aquantity of the clinically relevant chromosomal region from a referencequantity. A sequence imbalance can include chromosome dosage imbalance,allelic imbalance, mutation dosage imbalance, copy number imbalance,haplotype dosage imbalance, and other similar imbalances. As an example,an allelic imbalance can occur when a tumor has one allele of a genedeleted or one allele of a gene amplified or differential amplificationof the two alleles in its genome, thereby creating an imbalance at aparticular locus in the sample. As another example, a patient could havean inherited mutation in a tumor suppressor gene. The patient could thengo on to develop a tumor in which the non-mutated allele of the tumorsuppressor gene is deleted. Thus, within the tumor, there is mutationdosage imbalance. When the tumor releases its DNA into the plasma of thepatient, the tumor DNA will be mixed in with the constitutional DNA(from normal cells) of the patient in the plasma. Through the use ofmethods described herein, mutational dosage imbalance of this DNAmixture in the plasma can be detected.

The term “haplotype” as used herein refers to a combination of allelesat multiple loci that are transmitted together on the same chromosome orchromosomal region. A haplotype may refer to as few as one pair of locior to a chromosomal region, or to an entire chromosome. The term“alleles” refers to alternative DNA sequences at the same physicalgenomic locus, which may or may not result in different phenotypictraits. In any particular diploid organism, with two copies of eachchromosome (except the sex chromosomes in a male human subject), thegenotype for each gene comprises the pair of alleles present at thatlocus, which are the same in homozygotes and different in heterozygotes.A population or species of organisms typically includes multiple allelesat each locus among various individuals. A genomic locus where more thanone allele is found in the population is termed a polymorphic site.Allelic variation at a locus is measurable as the number of alleles(i.e., the degree of polymorphism) present, or the proportion ofheterozygotes (i.e., the heterozygosity rate) in the population. As usedherein, the term “polymorphism” refers to any inter-individual variationin the human genome, regardless of its frequency. Examples of suchvariations include, but are not limited to, single nucleotidepolymorphism, simple tandem repeat polymorphisms, insertion-deletionpolymorphisms, mutations (which may be disease causing) and copy numbervariations.

The term “sequenced tag” refers to a sequence determined from all orpart of a nucleic acid molecule, e.g., a DNA fragment. Often, just oneend of the fragment is sequenced, e.g., about 30 bp. The sequenced tagcan then be aligned to a reference genome. Alternatively, both ends ofthe fragment can be sequenced to generate two sequenced tags, which canprovide greater accuracy in the alignment and also provide a length ofthe fragment.

The term “universal sequencing” refers to sequencing where adapters areadded to the end of a fragment, and the primers for sequencing attachedto the adapters. Thus, any fragment can be sequenced with the sameprimer, and thus the sequencing can be random.

The term “size distribution” refers to any one value or a set of valuesthat represents a length, mass, weight, or other measure of the size ofmolecules corresponding to a particular group (e.g. fragments from aparticular haplotype or from a particular chromosomal region). Variousembodiments can use a variety of size distributions. In someembodiments, a size distribution relates to the rankings of the sizes(e.g., an average, median, or mean) of fragments of one chromosomerelative to fragments of other chromosomes. In other embodiments, a sizedistribution can relate to a statistical value of the actual sizes ofthe fragments of a chromosome. In one implementation, a statisticalvalue can include any average, mean, or median size of fragments of achromosome. In another implementation, a statistical value can include atotal length of fragments below a cutoff value, which may be divided bya total length of all fragments, or at least fragments below a largercutoff value.

The term “classification” as used herein refers to any number(s) orother characters(s) that are associated with a particular property of asample. For example, a “+” symbol (or the word “positive”) could signifythat a sample is classified as having deletions or amplifications. Theclassification can be binary (e.g., positive or negative) or have morelevels of classification (e.g., a scale from 1 to 10 or 0 to 1). Theterm “cutoff” and “threshold” refer to a predetermined number used in anoperation. For example, a cutoff size can refer to a size above whichfragments are excluded. A threshold value may be a value above or belowwhich a particular classification applies. Either of these terms can beused in either of these contexts.

The term “level of cancer” can refer to whether cancer exists, a stageof a cancer, a size of tumor, how many deletions or amplifications of achromosomal region are involved (e.g. duplicated or tripled), and/orother measure of a severity of a cancer. The level of cancer could be anumber or other characters. The level could be zero. The level of canceralso includes premalignant or precancerous conditions associated withdeletions or amplifications.

DETAILED DESCRIPTION

Cancerous tissue (tumor) can have aberrations, such as deletion oramplification of a chromosomal region. The tumor can release DNAfragments into fluids in the body. Embodiments can identify a tumor byanalyzing the DNA fragments to identify the aberrations relative tonormal (expected) values for DNA in the chromosomal region.

The exact size of the deletion or amplification can vary, as well as thelocation. There may be times when a particular region is known to showaberration in general for cancers or for a particular type of cancer(thereby leading to a diagnosis of a particular cancer). When aparticular region is not known, a systematic method for analyzing theentire genome or large parts of the genome may be employed to detectaberrant regions that may be dispersed throughout the genome and whosesize (e.g. number of bases deleted or amplified) varies. The chromosomalregion(s) can be tracked over time to identify changes in a severity ofan aberration or a number of regions showing an aberration. Thistracking can provide vital information for screening, prognosticating,and monitoring a tumor (e.g. after treatment or for detecting relapse ortumor progression).

This description first starts out with examples of chromosomalaberrations in cancer. Then, examples of ways to detect a chromosomalaberration by detecting and analyzing cell-free DNA in a biologicalsample is discussed. Once the methods of detecting an aberration in onechromosomal region is established, methods for detecting aberrations inmany chromosomal regions are used in systematic way to screen (diagnose)and prognosticate patients is described. This description also describesmethods for tracking numerical indicators obtained from tests forchromosomal aberration in one or more regions over time to providescreening, prognosticating, and monitoring of patients. Examples arethen discussed.

I. Examples of Chromosomal Aberrations in Cancer

Chromosomal aberrations are commonly detected in cancer cells. Moreover,characteristic patterns of chromosomal aberrations can be found inselected types of cancer. For example, gains of DNA in chromosome arms1p, 1q, 7q, 15q, 16p, 17q and 20q and losses of DNA at 3p, 4q, 9p and11q are commonly detected in hepatocellular carcinoma (HCC). Previousstudies have demonstrated that such genetic aberrations could also bedetected in the circulating DNA of cancer patients. For example, loss ofheterozygosity (LOH) has been detected for a particular locus in thecirculating DNA molecules of patients suffering from lung, and head andneck cancers (Chen X Q, et al. Nat Med 1996; 2: 1033-5; Nawroz H, et al.Nat Med 1996; 2: 1035-7). The genetic alterations detected in the plasmaor serum were identical to those found in the tumor tissues. However, astumor-derived DNA only contributes a minor fraction of total circulatingcell-free DNA, the allelic imbalance caused by LOH of tumor cells isusually small. A number of investigators have developed the digitalpolymerase chain reaction (PCR) technology (Vogelstein B, Kinzler K W.Proc Natl Acad Sci USA. 1999; 96: 9236-41; Zhou W, et al. Nat Biotechnol2001; 19: 78-81; Zhou W, et al. Lancet. 2002; 359: 219-25) for theaccurate quantification of different alleles of a locus among thecirculating DNA molecules (Chang H W, et al. J Natl Cancer Inst. 2002;94: 1697-703). Digital PCR is much more sensitive than real-time PCR orother DNA quantification methods for the detection of a small allelicimbalance caused by the LOH at a particular locus in the tumor DNA.However, digital PCR can still have difficulties in identifying a verysmall allelic imbalance at a particular locus, and thus embodimentsdescribed herein analyze chromosomal regions in a collective fashion.

The technology described herein also has applications for the detectionof premalignant or precancerous conditions. Examples of such conditionsincluding cirrhosis of the liver and cervical intraepithelial neoplasia.The former condition is a premalignant condition for hepatocellularcarcinoma while the latter condition is a premalignant condition forcervical carcinoma. It has been reported that such premalignantconditions already possess several of the molecular alterations in theirevolution to become a malignant tumor. For example, the presence of LOHat chromosome arms 1p, 4q, 13q, 18q and concurrent losses at more than 3loci are associated with an increased risk of HCC development inpatients with liver cirrhosis (Roncalli M et al. Hepatology 2000;31:846-50). Such premalignant lesions would also release DNA into thecirculation, although likely to be at lower concentrations. Thetechnology can allow detection of deletions or amplifications byanalyzing DNA fragments in plasma and to measure the concentration(including fractional concentration) of circulating premalignant DNA inplasma. The ease with which such aberrations are detected (e.g. depth ofsequencing or the number of such changes detected) and theconcentrations would predict the likelihood or rapidity of progressionto a full blown cancerous condition.

A. Deletion of a Chromosomal Region

FIG. 1 illustrates a chromosomal region of a cancer cell exhibiting anaberration of a deletion. The normal cell is shown with two haplotypes,Hap I and Hap II. As shown, both Hap I and Hap II have a sequence ateach of a plurality of heterozygous loci 110 (also referred to as singlenucleotide polymorphisms SNPs). In the cell associated with cancer, HapII has chromosomal region 120 deleted. As examples, the cell associatedwith cancer can be from a tumor (e.g., a malignant tumor), from ametastatic focus of the tumor (e.g. in a regional lymph node, or in adistant organ), or from a pre-cancerous or premalignant lesion, e.g., asis mentioned above.

In chromosomal region 120 of the cancer cell in which one of the twohomologous haplotypes is deleted, all the heterozygous SNPs 110 wouldappear as homozygous because of the loss of the other allele on thecorresponding deleted homologous chromosome. Therefore, this type ofchromosomal aberration is called loss of heterozygosity (LOH). In region120, the non-deleted alleles of these SNPs would represent one of thetwo haplotypes which can be found in the normal tissues. In the exampleshown in FIG. 1, the haplotype I (Hap I) at the LOH region 120 can bedetermined by genotyping the tumor tissue. The other haplotype (Hap II)can be determined by comparing the apparent genotypes of the normaltissues and the cancer tissues. Hap II can be constructed by joining allthe deleted alleles. That is all of the alleles in the normal cell forregion 120 that do not appear in region 120 for the cancer cell aredetermined to be on the same haplotype, i.e. Hap I. Through thisanalysis, the haplotypes of patients (e.g., hepatocellular carcinoma HCCpatients) can be determined for all chromosomal regions exhibiting LOHin the tumor tissue. Such a method is only useful if one has cancercells, and only works for determining the haplotype in region 120, butdoes provide a good illustration of a deleted chromosomal region.

B. Amplification of a Chromosomal Region

FIG. 2 illustrates a chromosomal region of a cancer cell exhibiting anaberration of an amplification. The normal cell is shown with twohaplotypes, Hap I and Hap II. As shown, both Hap I and Hap II have asequence at each of a plurality of heterozygous loci 210. In the tumorcell, Hap II has chromosomal region 220 amplified two times(duplicated).

Similarly, for regions with mono-allelic amplification in the tumortissues, the amplified alleles at the SNPs 210 can be detected bymethods such as microarray analysis. One of the two haplotypes (Hap IIin the example shown in FIG. 2) can be determined by joining up all theamplified alleles in chromosomal region 220. The amplified allele at aparticular locus can be determined by comparing the number of each ofthe alleles at the locus. Then, the other haplotype (Hap I) can bedetermined by joining up the non-amplified alleles. Such a method isonly useful if one has cancer cells, and only works for determining thehaplotype in region 220, but does provide a good illustration of anamplified chromosomal region.

An amplification can result from having more than 2 chromosomes, or therepeating of a gene in one chromosome. One region could be tandemlyduplicated, or a region could be a minute chromosome that contains oneor more copies of the region. The amplification could also result from agene of one chromosome being copied and inserted into a differentchromosome or a different region in the same chromosome. Such insertionsare a type of amplification.

II. Selection of a Chromosomal Region

As cancer tissue will contribute at least some of these cell-free DNA(and potentially cellular DNA), the genomic aberrations of the cancertissue can be detected in the sample such as plasma and serum. A problemwith detecting the aberration is that the tumour or cancer may be quitesmall, thereby providing relatively little DNA from the cancer cells.Thus the amount of circulating DNA with the aberration is quite small,thereby making detection very difficult. There may not be enough DNA ata single locus in the genome to detect an aberration. Methods describedherein can overcome this difficulty by analyzing DNA at a chromosomalregion including a plurality of loci (haplotype), thus changing a smallvariation at one locus into a perceptible difference when aggregatedover the haplotype. Thus, analyzing a plurality of loci of a region canprovide greater accuracy, and can reduce false positives and falsenegatives.

Also, the region of aberration may be quite small, thereby makingidentifying the aberration difficult. If just one locus or specific lociare used, aberrations not at those loci will be missed. Some methods, asdescribed herein, can investigate whole regions to find an aberrationwithin a subset of the region. When the analyzed regions span thegenome, the whole genome can thus be analyzed to find aberrations ofvarying length and location, as is described in more detail below.

To illustrate these points, as shown above, a region can have anaberration. But, a region must be selected for analysis. The length andposition of a region can alter the results, and thus affect theanalysis. For example, if the first region in FIG. 1 is analyzed, noaberration would be detected. If the second region is analyzed, anaberration can be detected, e.g., using methods described herein. If alarger region including both the first and second region were analyzed,one encounters the difficulty that only part of the larger region has anaberration, which may make identifying any aberration more difficult, aswell as the problem of identifying the exact location and length of theaberration. Various embodiments can address some and/or all of thesedifficulties. The description for selecting a region is equallyapplicable to methods that use haplotypes of a same chromosomal region,or that use two different chromosomal regions.

A. Selecting a Particular Chromosomal Region

In one embodiment, a particular region could be selected based on theknowledge of the cancer or the patient. For example, the region could beknown to commonly exhibit aberration in many cancers or a particularcancer. The exact length and position of the region can be determined byreferring to the literature regarding what is well known for the cancertype or for patients with particular risk factors. Additionally, thetumor tissues of the patient can be obtained and analyzed to identifyregions of aberrations, as is described above. Now such a techniquewould require obtaining a cancer cell (which may not be practical forpatients just being diagnosed), but such a technique can be used toidentify regions for monitoring over time in the same patient (e.g.,after surgery to remove cancerous tissue, or after chemotherapy orimmunotherapy or targeted therapy, or for detecting tumor relapse orprogression).

One could identify more than one particular region. The analysis of eachone of such regions can be used independently, or the different regionscan be analyzed collectively. Additionally, a region may be subdividedto provide greater accuracy in locating an aberration.

FIG. 3 shows a table 300 illustrating different types of cancers andassociated regions and their corresponding aberrations. Column 310 liststhe different cancer types. Embodiments described herein can be used forany type of cancer involving an aberration, and thus this list is justof examples. Column 320 shows regions (e.g., large regions, such as 7por more specific regions of 17q25) where a gain (amplification) isassociated with the particular cancer of the same row. Column 330 showsregions where losses (deletions) can be found. Column 340 listsreferences that discuss the association of these regions with theparticular cancer.

These regions with potential chromosomal aberrations can be used as thechromosomal region(s) for analysis according to methods describedherein. Examples of other genomic regions altered in cancer can be foundat the database of the Cancer Genome Anatomy Project(cgap.nci.nih.gov/Chromosomes/RecurrentAberrataions) and the Atlas ofGenetics and Cytogenetics in Oncology and Haematology(atlasgeneticsoncology.org Tumors/Tumorliste.html).

As one can see, the identified regions can be quite large while othermay be more specific. The aberration may not include the entire regionidentified in the table. Thus, such clues about the type of aberrationsdoes not pinpoint exactly where the aberration actually exists for aparticular patient, but may be more used as a rough guide about largeregions for analysis. Such large regions may include many subregions(which may be of equal size) that are analyzed individually as well ascollectively in the larger region (details of which are describedherein). Thus, embodiments may combine aspects of selecting a largeregion based on particulars of the cancer to be tested, but may alsoemploy more general techniques (e.g., testing the subregions) as isdescribed next.

B. Selecting an Arbitrary Chromosomal Region

In another embodiment, a chromosomal region being analyzed is chosenarbitrarily. For example, the genome could be separated into regionsthat are one megabase (Mb) in length, or other predetermined segmentlengths such as 500 Kb or 2 Mb. If the regions are 1 Mb, then there areapproximately 3,000 regions in the human genome, since there are about 3billion bases in the haploid human genome. These regions can then eachbe analyzed, as is discussed in more detail later.

Such regions may be determined, not based on any knowledge of cancer orthe patient, but based on a systematic partitioning of the genome intoregions to be analyzed. In one implementation, when a chromosome doesnot have a length that is a multiple of the predetermined segment (e.g.,not divisible by 1 million bases), the last region of a chromosome couldbe less than the predetermined length (e.g. less than 1 MB). In anotherimplementation, each chromosome could be separated into regions of equallength (or approximately equal—within rounding error) based on the totallength of the chromosome and the number of segments to be created (whichwould typically vary among the chromosomes). In such an implementation,the length of the segments of each chromosome could differ.

As mentioned above, a particular region can be identified based on aspecific cancer being tested, but then the particular region can besubdivided into smaller regions (e.g. subregions of equal size that spanthe particular larger region). In this manner, aberrations may bepinpointed. In the discussion below, any general reference to achromosomal region may be a region that is specifically identified, aregion that is chosen arbitrarily, or a combination of both.

III. Detection of Aberrations in a Particular Haplotype

This section describes methods for detecting an aberration in a singlechromosomal region by analyzing a biological sample that includescell-free DNA. In the embodiments of this section, the singlechromosomal region is heterozygous (different alleles) at a plurality ofloci in the region, thereby providing two haplotypes that can bedistinguished by knowing the particular allele at a given locus. Thus, agiven nucleic acid molecule (e.g., a fragment of cell-free DNA) can beidentified as being from a particular one of the two haplotypes. Forexample, the fragment can be sequenced to obtain a sequence tag thataligns to the chromosomal region, and then the haplotype at aheterozygous loci to which the allele belongs can be identified. Twogeneral types of techniques are described below for determiningaberrations in a particular haplotype (Hap), specifically tag countingand size analysis.

A. Determining Haplotypes

To differentiate between the two haplotypes, the two haplotypes of achromosomal region are first determined. For example, the two haplotypesHap I and Hap II shown in the normal cell of FIG. 1 can be determined.In FIG. 1, the haplotypes include a first plurality of loci 110, whichare heterozygous, and allow for a differentiation between the twohaplotypes. This first plurality of loci span the chromosomal regionbeing analyzed. The alleles on the different heterozygous loci (hets)can first be determined and then phased to determine the haplotypes ofthe patient.

The haplotype of the SNP alleles can be determined by single moleculeanalysis methods. Examples of such methods have been described by Fan etal (Nat Biotechnol. 2011; 29:51-7), Yang et al (Proc Natl Acad Sci USA.2011; 108:12-7) and Kitzman et al (Nat Biotechnol. 2011 January;29:59-63). Alternatively, the haplotypes of an individual can bedetermined by the analysis of the genotypes of the family members (e.g.parents, siblings, and children). Examples include the methods describedby Roach et al (Am J Hum Genet. 2011; 89(3):382-97) and Lo et al (SciTransl Med. 2010; 2:61ra91). In yet another embodiment, the haplotype ofthe individuals can be determined by comparing the genotyping results ofthe tumor tissues and the genomic DNA. The genotype of these subjectscan be performed by microarray analysis, such as using t

Haplotypes can also be constructed by other methods well known to thoseskilled in the art. Examples of such methods include those based onsingle molecule analysis such as digital PCR (Ding C and Cantor C R.Proc Natl Acad Sci USA 2003; 100: 7449-7453; Ruano G et al. Proc NatlAcad Sci USA 1990; 87: 6296-6300), chromosome sorting or separation(Yang H et al. Proc Natl Acad Sci USA 2011; 108: 12-17; Fan H C et al.Nat Biotechnol 2011; 29: 51-57), sperm haplotyping (Lien S et al. CurrProtoc Hum Genet 2002; Chapter 1:Unit 1.6) and imaging techniques (XiaoM et al. Hum Mutat 2007; 28: 913-921). Other methods include those basedon allele-specific PCR (Michalatos-Beloin S et al. Nucleic Acids Res1996; 24: 4841-4843; Lo Y M D et al. Nucleic Acids Res 19: 3561-3567),cloning and restriction enzyme digestion (Smirnova A S et al.Immunogenetics 2007; 59:93-8), etc. Yet other methods are based on thedistribution and linkage disequilibrium structure of haplotype blocks inthe population which allow the subject's haplotype to be inferred fromstatistical assessments (Clark A G. Mol Biol Evol 1990; 7:111-22;10:13-9; Salem R M et al. Hum Genomics 2005; 2:39-66).

Another method of determining the haplotype of the region of LOH is bygenotyping the normal tissues and the tumoral tissues of the subject ifthe tumoral tissues are available. In the presence of LOH, tumoraltissues with a very high fractional concentration of tumor cells wouldshow an apparent homozygosity for all the SNP loci within the regionshowing LOH. The genotypes of these SNP loci would comprise onehaplotype (Hap I of the LOH region in FIG. 1). On the other hand, thenormal tissues would indicate that the subject is heterozygous for theSNP loci within the region of LOH. The alleles that are present in thenormal tissues but not the tumoral tissues would comprise the otherhaplotype (Hap II of the LOH region in FIG. 1).

B. Relative Haplotype Dosage (RHDO) Analysis

As mentioned above, chromosomal aberrations with amplification ordeletion of one of the haplotypes of a chromosome region would lead toan imbalance of the dosage of the two haplotypes in the chromosomeregion in the tumor tissues. In the plasma of a person with a tumoralgrowth, a fraction of the circulating DNA is derived from the tumorcells. Due to the presence of tumor-derived DNA in the plasma of cancerpatients, such imbalance would also be present in their plasma. Theimbalance in the dosage of the two haplotypes can be detected throughcounting the number of molecules coming from each haplotype.

For the chromosomal regions in which LOH is observed in the tumortissues (e.g. region 120 of FIG. 1), Hap I would be over-representedamong the circulating DNA molecules (fragments) when compared with HapII because of the lack of contribution of Hap II from the tumor tissues.For the chromosomal regions in which the copy number amplification isobserved in the tumor tissues, Hap II would be over-represented whencompared with Hap I for the regions affected by mono-allelicamplification of Hap II because of the release of an additional dose ofHap II from the tumor tissues. To determine an over or underrepresentation, certain DNA fragments in a sample are determined to befrom Hap I or Hap II, which can be done by a variety of methods, e.g. byperforming universal sequencing and aligning or using digital PCR andsequence-specific probes.

After sequencing of a plurality of DNA fragments from the plasma (orother biological sample) of the cancer patients to generate sequencedtags, the sequenced tags corresponding to the alleles on the twohaplotypes can be identified and counted. The numbers of sequenced tagscorresponding to each of the two haplotypes can then be compared todetermine if the two haplotypes are equally represented in the plasma.In one embodiment, sequential probability ratio testing (SPRT) can beused to determine if the representations of the two haplotypes in plasmaare significantly different. A statistically significant differencesuggests the presence of a chromosomal aberration at the analyzedchromosomal region. In addition, the quantitative difference of the twohaplotypes in plasma can be used for the estimation of the fractionalconcentration of tumor-derived DNA in the plasma, as is described below.

The diagnostic approaches for determining an identity of a DNA fragment(e.g. its location in the human genome) described in this applicationare not limited to using massively parallel sequencing as the detectionplatform. These diagnostic approaches can also be applied to, forexample but not limited to, microfluidics digital PCR systems (e.g.Fluidigm digital array system, microdroplet digital PCR system (e.g.those from RainDance and QuantaLife), the BEAM-ing system (i.e. beads,emulsion PCR, amplification and magnetics) (Diehl et al. Proc Natl AcadSci USA 2005; 102: 16368-16373), real-time PCR, mass spectrometry basedsystems (e.g. the SequenomMassArray system) and multiplexligation-dependent probe amplification (MLPA) analysis.

Normal Regions

FIG. 4 illustrates chromosomal regions within a cancer cell that do notexhibit an aberration along with measurements made in plasma accordingto embodiments of the present invention. The chromosomal region 410 maybe selected by any method, e.g., based on a specific cancer to be testedor based on a general screening that uses predetermined segments thatspan large sections of the genome. To differentiate between the twohaplotypes, the two haplotypes are first determined. FIG. 4 shows twohaplotypes (Hap I and Hap II) of a normal cell for chromosomal region410. The haplotypes include a first plurality of loci 420. This firstplurality of loci 420 span the chromosomal region 410 being analyzed. Asshown, these loci are heterozygous in the normal cell. The twohaplotypes for a cancer cell are also shown. In the cancer cell, noregions are deleted or amplified.

FIG. 4 also shows the number of allelic counts on each haplotype foreach of the loci 420. A cumulative total is also provided for certainsubregions of chromosomal region 410. The number of allelic countscorresponds to the number of DNA fragments that correspond to theparticular haplotype at each the particular locus. For example, a DNAfragment that includes the first loci 421 and has the allele A would getcounted toward Hap I. And, a DNA fragment with allele T would getcounted toward Hap II. The determination of where a fragment aligns(i.e. whether it includes a particular locus) and what allele itcontains can be determined in various ways, as is mentioned herein. Aratio of the counts on the two haplotypes may be used to determinewhether a statistically significant different exists. This ratio iscalled an odds ratio, herein. A difference between the two values mayalso be used; the difference may be normalized by a total number offragments. The ratio and difference (and functions thereof) are examplesof parameters that are compared to thresholds to determineclassifications of whether an aberration exists.

The RHDO analysis can make use of all alleles on the same haplotype(e.g. cumulative counts) for determining if there is any imbalance ofthe two haplotypes in the plasma, e.g., as can be done in maternalplasma as described in the Lo patent application Ser. Nos. 12/940,992and 12/940,993, referred to above. This method can significantlyincrease the number of DNA molecules used for determining if there isany imbalance and, hence, results in better statistical power fordifferentiating an imbalance due to the presence of cancer fromstochastic distribution of allelic counts in the absence of cancer or apre-malignant condition. In contrast to analyzing multiple SNP lociseparately, the RHDO approach can make use of the relative position ofthe alleles on the two chromosomes (haplotype information) such that thealleles located on the same chromosome can be analyzed together. In theabsence of the haplotype information, the allele counts of different SNPloci cannot be added together to statistically determine if a haplotypeis over- or under-represented in plasma. The quantification of theallelic counts can be performed by, but not limited to, massivelyparallel sequencing (e.g. using the Illumina sequencing by synthesissystem, the sequencing by ligation technology (SOLiD) by LifeTechnologies, the Ion Torrent sequencing system by Ion Torrent and LifeTechnologies, nanopore sequencing (nanoporetech.com), and the 454sequencing technology (Roche), digital PCR (e.g. by microfluidicsdigital PCR (for example Fluidigm (fluidigm.com)) or the BEAMing (beads,emulsion PCR, amplification, magnetics (inostics.com)) or droplet PCR(e.g. by QuantaLife (quantalife.com) and RainDance(raindancetechnologies.com)) and real-time PCR. In other implementationof the technology, enriched target sequencing using in-solution capture(e.g. using the Agilent SureSelect system, the Illumina TruSeq CustomEnrichment Kit(illumina.com/applications/sequencing/targeted_resequencing.ilmn), or bythe MyGenostics GenCap Custom Enrichment system (mygenostics.com/)) orarray-based capture (e.g. using the Roche NimbleGen system) can be used.

In the example shown in FIG. 4, a slight allelic imbalance is observedfor the first two SNP loci (24 vs 26 for the first SNP and 18 vs 20 forthe second SNP). However, number of allelic counts is not statisticallysufficient to determine if a real allelic imbalance is present. Thus,the counts of alleles on the same haplotype are added together until thecumulative allelic counts for the two haplotypes are sufficient toconclude statistically that no allelic imbalance between the twohaplotypes is present for chromosomal region 410 (the fifth SNP for thisexample). After a statistically significant classification is arrived,the cumulative count is reset (at the sixth SNP for this example). Thecumulative count is then determined until the cumulative allelic countsfor the two haplotypes are again sufficient to conclude statisticallythat no allelic imbalance between the two haplotypes for that particularsubregion of region 410. A total cumulative count could be used for theentire region as well, but the previous method can allow differentsubregions to be tested, which provides for greater precision (i.e. asubregion) in determining a location of an aberration, as opposed to theentire region 410. Examples of statistical tests for determining whethera real allelic imbalance is present include, but not limited to, thesequential probability ratio testing (Zhou W, et al. Nat Biotechnol2001; 19: 78-81; Zhou W, et al. Lancet. 2002; 359: 219-25), the t-test,and the chi-square test.

Detecting Deletions

FIG. 5 illustrates the deletion of chromosomal region 510 within acancer cell along with measurements made in plasma to determine thedeleted region according to embodiments of the present invention. FIG. 5shows two haplotypes (Hap I and Hap II) of a normal cell for chromosomalregion 510. The haplotypes include a first plurality of heterozygousloci 520 that span the chromosomal region 510 being analyzed. The twohaplotypes for a cancer cell are also shown. In the cancer cell, region510 is deleted for Hap II. As with FIG. 4, FIG. 5 also shows the numberof allelic counts for each of the loci 520. A cumulative total is alsokept for certain subregions within chromosomal region 510.

As tumor tissues typically contain a mixture of tumor cells andnon-tumor cells, the LOH may be manifested by skewing of the ratio ofthe amounts of the two alleles at loci within the region 510. In such asituation, the deleted haplotype Hap II in region 510 can be determinedby the combination of loci 520 which show a relative reduction in theamounts of DNA fragments, when compared with the corresponding loci onthe normal tissues. The haplotype with fragments that appear more oftenis Hap I, which is retained in the tumor cells. In certain embodiments,it might be desirable to perform a procedure that would enrich theproportion of tumor cells in the tumor sample, so as to allow thedeleted and retained haplotypes to be determined more readily. Oneexample of such a procedure is microdissection (either manually or bylaser capture techniques).

Theoretically, each of the alleles on Hap I would be over-represented inthe circulating DNA for the chromosomal region exhibiting LOH in thetumor tissues and the degree of allelic imbalance would be dependent onthe fractional concentration of tumor DNA in the plasma. However, at thesame time, the relative abundance of the two alleles in any circulatingDNA sample would also be governed by the Poisson distribution. Astatistical analysis can be performed to determine if an observedallelic imbalance is due to the presence of LOH in cancer tissues or dueto chance. The power of detecting real allelic imbalance associated withLOH in cancer is dependent on the number of circulating DNA moleculesbeing analyzed and the fractional concentration of tumoral DNA. A higherfractional concentration of tumoral DNA and a larger number of moleculesanalyzed would give rise to higher sensitivity and specificity fordetecting real allelic imbalance.

In the example shown in FIG. 5, a slight allelic imbalance is observedfor the first two SNP loci (24 vs 22 for the first SNP and 18 vs 15 forthe second SNP). However, number of allelic counts is not statisticallysufficient to determine if a real allelic imbalance is present. Thus,the counts of alleles on the same haplotype are added together until thecumulative allelic counts for the two haplotypes are sufficient toconclude statistically that an allelic imbalance between the twohaplotypes is present in region 510 (the fifth SNP for this example). Insome embodiments, only an imbalance is known, and the specific type(deletion of amplification) is not determined. The cumulative count isthen determined until the cumulative allelic counts for the twohaplotypes are again sufficient to conclude statistically that anallelic imbalance between the two haplotypes for that particularsubregion of region 510. A total cumulative count could be used for theentire region as well, as which may be done in any method describedherein.

Detecting Amplification of a Chromosomal Region

FIG. 6 illustrates the amplification of chromosomal region 610 within acancer cell along with measurements made in plasma to determine theamplified region according to embodiments of the present invention. Inaddition to LOH, amplification of chromosomal regions is also frequentlyobserved in cancer tissues. In the example shown in FIG. 6, Hap II inchromosomal region 610 is amplified to three copies in the cancer cell.As shown, region 610 includes only six heterozygous loci as opposed tothe longer regions shown in previous figures. The amplification isdetected as being statistically significant in the sixth loci, where theover-representation is determined to be statistically significant. Insome embodiments, only an imbalance is known, and the specific type(deletion or amplification) is not determined. In other embodiments, acancer cell may be obtained and analyzed. Such analysis can provideinformation about whether the imbalance is due to a deletion (cancercell is homozygous for deleted region) or an amplification (cancer cellis heterozygous for amplified region). In other implementations, whethera deletion or amplification exists can be determined using the methodsof section IV to analyze the entire region (i.e. not the haplotypesindividually). If the region is over-represented, then the aberration isan amplification; and if the region is under-represented, thenaberration is a deletion. Region 620 is also analyzed and the cumulativecounts confirm that no imbalance exists.

SPRT Analysis for the Plasma RHDO Analysis

For any chromosomal regions that have heterozygous loci, RHDO analysiscan be used to determine if there is any dosage imbalance of the twohaplotypes in the plasma. In these regions, the presence of haplotypedosage imbalance in plasma is suggestive of the presence oftumor-derived DNA in the plasma sample. In one embodiment, SPRT analysiscan be used to determine if the difference in the number of sequencedreads for Hap I and Hap II is statistically significant. In this exampleof SPRT analysis, we first determine the number of sequenced readscoming from each of the two haplotypes. Then we can determine aparameter (e.g. a fraction) that represents the proportional amount ofsequenced reads contributed by the potentially over-representedhaplotype (e.g. a fraction of the number of reads for one haplotypedivided by the number of reads for the other haplotype). The potentiallyover-represented haplotype would be the non-deleted haplotype in thescenario of LOH and the amplified haplotype in the scenario ofmono-allelic amplification of a chromosomal region. Then, this fractionwould be compared with two threshold values (the upper and lowerthresholds) which are constructed based on the null hypothesis, i.e. theabsence of haplotype dosage imbalance, and the alternative hypothesis,i.e. the presence of haplotype dosage imbalance. If the fraction isgreater than the upper threshold, it indicates the presence of astatistically significant imbalance of the two haplotypes in plasma. Ifthe fraction is below the lower threshold, it indicates that nostatistically significant imbalance of the two haplotypes is present. Ifthe fraction is between the upper and lower thresholds, it indicatesthat there is not sufficient statistical power to make a conclusion. Asequential increase in the number of heterozygous loci for the regionbeing analyzed may be performed until a successful SPRT classificationcan be made.

The equations for calculating the upper and lower boundaries of the SPRTare: Upper threshold=[(ln 8)/N−ln δ]/ln γ; Lower threshold=[(ln ⅛)/N−lnδ]/ln γ, where δ=(1−θ₁/(1−θ₂) and

${\gamma = \frac{\theta_{1}\left( {1 - \theta_{2}} \right)}{\theta_{2}\left( {1 - \theta_{1}} \right)}},$θ₁ is the expected fraction of sequenced tags from the potentiallyover-represented haplotype when allelic imbalance is present in plasma,θ₂ is the expected fraction of any of the two haplotypes when allelicimbalance is not present, i.e. 0.5, N is the total number of sequencedtags for Hap I and Hap II, in is a mathematical symbol representing thenatural logarithm, i.e. log_(e)). θ₁ would be dependent on thefractional concentration of tumor-derived DNA (F) that one expects (orknows) to be present in the plasma sample.

In the scenario of LOH, θ₁=1/(2−F). In the scenario of mono-allelicamplification, θ₁=(1+zF)/(2+zF), where z represents the number of theextra copy of the chromosomal region that is amplified in the tumor. Forexample, if one chromosome is duplicated, there would be one extra copyof the particular chromosome. Then, z is equal to 1.

FIG. 7 shows an RHDO analysis of the plasma DNA of the HCC patient for asegment located at chromosome 1p which showed mono-allelic amplificationin the tumor tissue according to embodiments of the present invention.The green triangles represent the patient's data. The total number ofsequenced reads increased with the increasing number of SNPs beinganalyzed. The fraction of the total sequenced reads from the amplifiedhaplotype in the tumor varied with the increasing number of the totalsequenced reads analyzed and eventually reached a value higher than theupper threshold. This indicates that a significant haplotype dosageimbalance and, hence, supports the presence of this cancer-associatedchromosomal aberration in plasma.

RHDO analysis using SPRT was performed for all chromosomal regions ofthe HCC patient showing amplifications and deletions in the tumortissue. The results are as follows for 922 segments known to have LOHand 105 segments known to have amplification. For LOH, 922 segments wereclassified with SPRT, and 921 of the segments were correctly identifiedas having haplotype dosage imbalance in plasma, to provide an accuracyof 99.99%. For mono-allelic amplification, 105 segments were classifiedwith SPRT, and 105 of the segments were correctly identified as havinghaplotype dosage imbalance in plasma, to provide an accuracy of 100%.

C. Relative Haplotype Size Analysis

As an alternative to counting dosage of the fragments aligned to the twohaplotypes, the size of the fragments for the respective haplotypes canbe used. For example, for a particular chromosomal region, the size ofthe DNA fragments from one haplotype can be compared to the size of theDNA fragments of the other haplotype. One can analyze the sizedistribution of DNA fragments that correspond to any of the alleles atheterozygous loci of a first haplotype of the region, and compare it tothe size distribution of DNA fragments that correspond to any of thealleles at the heterozygous loci of the second haplotype. Astatistically significant difference in the size distribution can beused to identify an aberration, in a similar manner that the number ofcounts can.

It has been reported that the size distribution of the total (i.e.tumoral plus non-tumoral) plasma DNA is increased in cancer patients(Wang B G, et al. Cancer Res. 2003; 63: 3966-8). However, if one isspecifically studying the tumor-derived DNA (instead of the total (i.e.tumor plus non-tumor) amount of DNA), then it has been observed that thesize distribution of tumor-derived DNA molecules is shorter than that ofmolecules derived from non-tumor cells (Diehl et al. Proc Natl Acad SciUSA. 2005; 102:16368-73). Therefore, the size distribution ofcirculating DNA can be used for determining if cancer-associatedchromosomal aberrations are present. The principle of the size analysisis shown in FIG. 8.

FIG. 8 shows the change in size distribution of fragments for twohaplotypes of a chromosomal region when a tumor containing a deletion ispresent according to embodiments of the present invention. Asillustrated in FIG. 8, the T allele is deleted in the tumor tissues. Asa result, the tumor tissues would only release short molecules of the Aallele into the plasma. The tumor-derived short DNA molecules would leadto the overall shortening of the size distribution for the A allele inthe plasma, hence, resulting in a shorter size distribution of the Aallele compared with the T allele in the plasma. As discussed in theprevious sections, all the alleles located on the same haplotype can beanalyzed together. In other words, the size distribution for DNAmolecules carrying alleles located on one haplotype can be compared withthat for DNA molecules carrying alleles on the other haplotype. Thedeleted haplotype in the tumor tissues would show a longer sizedistribution in plasma.

Size analysis can also be applied for detecting amplification ofchromosomal regions associated with cancer. FIG. 9 shows the change insize distribution of fragments for two haplotypes of a chromosomalregion when a tumor containing an amplification is present according toembodiments of the present invention. In the example shown in FIG. 9,the chromosomal region carrying the allele T is duplicated in the tumor.As a result, increased amount of short DNA molecules carrying the Tallele would be released in the plasma, hence, resulting in an overallshortening of the size distribution of the T allele compared with thesize distribution of the A allele. The size analysis can be appliedcollectively to all the alleles located on the same haplotype. In otherwords, the size distribution for the haplotype amplified in the tumortissues would be shorter than the size distribution for the haplotypenot amplified in the tumor.

Detection of the Shortening of the Size Distribution of Circulating DNA

The size of the DNA fragments arising from the two haplotypes, namelyHap I and Hap II, can be determined by, but not limited to, paired-endmassively parallel sequencing. After sequencing the ends of a DNAfragment, the sequenced reads (tags) can be aligned to the referencehuman genome. The size of the sequenced DNA molecules can be inferredfrom the coordinates of the outermost nucleotide at each end. Thesequenced tags of the molecule can be used to determine if the sequencedDNA fragment arises from Hap I or Hap II. For example, one of thesequenced tags may include a heterozygous locus in the chromosomalregion being analyzed.

Therefore, for each sequenced molecule, we can determine both the sizeand whether it arises from Hap I or Hap II. Based on the size of thefragments aligned to each haplotype, a computer system can calculate thesize distribution profiles (e.g. an average fragment size) for both HapI and Hap II. The size distributions of DNA fragments from Hap I and HapII can be compared using appropriate statistical analysis to determinewhen the size distributions are sufficiently different to identify anaberration. Apart from paired-end massively parallel sequencing, othermethods can be used for determining the size of the DNA fragments,including but not limited to, sequencing whole DNA fragments, massspectrometry, and optical methods for observing and comparing thelengths of the observed DNA molecules to a standard.

Next, we introduce two example methods for detecting the shortening ofcirculating DNA associated with genetic aberrations of tumors. These twomethods aim to provide a quantitative measurement of the difference insize distribution for two populations of DNA fragments. The twopopulations of DNA fragments refer to the DNA molecules corresponding toHap I and Hap II.

Difference in the Fraction of Short DNA Fragments

In one implementation, a fraction of short DNA fragments is used. Onesets a cutoff size (w) to define the short DNA molecules. The cutoffsize can be varied and be chosen to fit different diagnostic purposes. Acomputer system can determine the number of molecules that are equal toor shorter than the size cutoff. The fraction of DNA fragments (Q) canthen be calculated by dividing the number of short DNA by the totalnumber of DNA fragments. The value of Q would be affected by the sizedistribution of the population of DNA molecules. A shorter overall sizedistribution signifies that a higher proportion of the DNA moleculeswould be short fragments, thus, giving a higher value of Q.

The difference in the fraction of short DNA fragments between Hap I andHap II can then be used. The difference in the size distribution of DNAfragments from Hap I and Hap II can be reflected by the difference inthe fraction of short fragments for Hap I and Hap II (ΔQ).ΔQ=Q_(HapI)−Q_(HapII), where Q_(HapI) is the fraction of short fragmentsfor Hap I DNA fragments; and Q_(HapII) is the fraction of shortfragments for Hap II DNA fragments. Q_(HapI) and Q_(HapII) are examplesof a statistical value of the two groups of the size distributions offragments from each of the haplotypes.

As illustrated in the previous section, when Hap II is deleted in thetumor tissues, the size distribution for Hap I DNA fragments would beshorter than that for Hap II DNA fragments. As a result, a positivevalue of ΔQ would be observed. The positive value of ΔQ can be comparedto a threshold value to determine if ΔQ is large enough for a deletionto be considered as existing. An amplification of Hap I would also showa positive value of ΔQ. When there is a duplication of Hap II in thetumor tissues, the size distribution for Hap II DNA fragments would beshorter than that for Hap I DNA fragments. Hence, the value of ΔQ wouldbecome negative. In the absence of chromosomal aberration, the sizedistribution for Hap I and Hap II DNA fragments in plasma/serum would besimilar. Hence, the value of ΔQ would be approximately zero.

The ΔQ of a patient can be compared with normal individuals to determineif the value is normal. In addition or alternatively, the ΔQ value of apatient can be compared with values obtained from patients with similarcancers to determine if the value is abnormal. Such comparison caninvolve comparison(s) to threshold values as described herein. In thecontext of disease monitoring, the value ΔQ can be monitored seriallyover time. The change in the value of ΔQ may indicate the increasedfractional concentration of tumoral DNA in plasma/serum. In selectedimplementation of this technology, the fractional concentration oftumoral DNA can be correlated with tumor stage, prognostication andprogression of the disease. Such implementations using measurements atdifferent times is discussed in more detail later.

Difference in the Fraction of Total Length Contributed by Short DNAFragments

In this implementation, the fraction of total length contributed byshort DNA fragments is used. A computer system can determine the totallength of a group of DNA fragments in a sample (e.g. the fragments froma particular haplotype of a given region or just from the given region).A cutoff size (w) below which the DNA fragments are defined as “shortfragments” can be chosen. The cutoff size can be varied and be chosen tofit different diagnostic purposes. Then, a computer system can determinethe total length of the short DNA fragments by summing up the length ofthe random selection of DNA fragments that are equal to or shorter thanthe cutoff size. The fraction of total length contributed by short DNAfragments can then be calculated as follows: F=Σ^(w)length/Σ^(N)length,where Σ^(w) length represents sum of the lengths of DNA fragments withlength equal to or less than w(bp); and Σ^(N) length represents the sumof the length of DNA fragments equal to or less than a predeterminedlength N. In one embodiment, N is 600 bases. However, other size limits,e.g. 150 bases, 180 bases, 200 bases, 250 bases, 300 bases, 400 bases,500 bases and 700 bases, can be used for calculating the “total length”.

A value of 600 bases or below may be chosen because the Illumina GenomeAnalyzer system is not effective in amplifying and sequencing DNAfragments longer than 600 bases. In addition, limiting the analysis toDNA fragments of shorter than 600 bases can also avoid biases arisingfrom structural variations of the genome. In the presence of structuralvariations, for example rearrangements (Kidd J M et al, Nature 2008;453:56-64), the size of the DNA fragment can be overestimated when thesize is estimated bioinformatically by mapping the ends of the DNAfragment to the reference genome. In addition, >99.9% of all the DNAfragments successfully sequenced and mapped to the reference genome areless than 600 bases and, thus, including all fragments equal to andshorter than 600 bases would provide a representative estimation of thesize distribution of the DNA fragments in the sample.

Accordingly, the difference in the fractions of total length contributedby short DNA fragments between Hap I and Hap II can be used. Theperturbation in the size distribution between Hap I and Hap II DNAfragments can be reflected by the difference in their F values. Here wedefine F_(Hap I) and F_(Hap II) as the fractions of total lengthcontributed by short DNA fragments for Hap I and Hap II, respectively.The difference in the fractions of total length contributed by short DNAfragments between Hap I and Hap II (ΔF) can be calculated as:ΔF=F_(Hap I)−F_(Hap II). F_(HapI) and F_(HapII) are examples of astatistical value of the two groups of the size distributions offragments from each of the haplotypes.

Similar to embodiments illustrated in the previous section, the deletionof Hap II in the tumor tissues would lead to the apparent shortening ofthe size distribution for Hap I DNA fragments when compared with Hap IIDNA fragments. This would lead to a positive value of ΔF. When Hap II isduplicated, a negative ΔF value would be observed. In the absence ofchromosomal aberration, the value of ΔF would be approximately zero.

The ΔF of a patient can be compared with normal individuals to determineif the value is normal. The ΔF of a patient can be compared with valuesobtained from patients with similar cancers to determine if the value isabnormal. Such comparison can involve comparison(s) to threshold valuesas described herein. In the context of disease monitoring, the value ΔQcan be monitored serially. The change in the value of ΔF may indicatethe increased fractional concentration of tumoral DNA in plasma/serum.

D. General Method

FIG. 10 is a flowchart illustrating a method of analyzing the haplotypesof a biological sample of an organism to determine whether a chromosomalregion exhibits a deletion or an amplification according to embodimentsof the present invention. The biological sample includes nucleic acidmolecules (also called fragments) originating from normal cells andpotentially from cells associated with cancer. These molecules may becell-free in the sample. The organism can be of any type that has morethan one copy of a chromosome, i.e., at least diploid organisms, but caninclude higher polyploid organisms.

In one embodiment of this and any other method described herein, thebiological sample includes cell-free DNA fragments. Although theanalysis of plasma DNA has been used to illustrate the different methodsdescribed in this application, these methods can also be applied todetect tumor-associated chromosomal aberrations in samples containing amixture of normal and tumor-derived DNA. The other sample types includesaliva, tears, pleural fluid, ascitic fluid, bile, urine, serum,pancreatic juice, stool and cervical smear samples

In step 1010, first and second haplotypes are determined for normalcells of the organism at a first chromosomal region. The haplotypes canbe determined by any suitable method, such as those mentioned herein.The chromosomal region may be selected via any method, e.g., methodsdescribed herein. The first chromosomal region includes a firstplurality of loci (e.g., loci 420 of region 410) that are heterozygous.The heterozygous loci (hets) may be far apart from each other, e.g., theloci can be 500 or 1000 bases (or more) apart from another locus of thefirst plurality of loci. Other hets may exist in the first chromosomalregion, but not be used.

In step 1020, a plurality of nucleic acid molecules in the biologicalsample are characterized regarding location and allele of each molecule.For instance, a location of a nucleic acid molecule in a referencegenome of the organism can be identified. This locating can be performedin various ways, including performing a sequencing of a molecule (e.g.via universal sequencing), to obtain one or two (paired-end) sequencedtags of the molecule and then aligning the sequenced tag(s) to thereference genome. Such alignment can be performed using such as tools asbasic local alignment search tool (BLAST). The location can beidentified as a number in an arm of a chromosome. The allele at one ofthe heterozygous loci (hets) can be used to determine which haplotype afragment is from.

In step 1030, a first group of nucleic acid molecules are identified asbeing from the first haplotype based on the identified locations anddetermined alleles. For example, a fragment including loci 421 of FIG. 4having allele A would be identified as being from Hap I. The first groupcan span the first chromosomal region by including at least one nucleicacid molecule located at each of the first plurality of loci.

In step 1040, a second group of nucleic acid molecules are identified asbeing from the second haplotype based on the identified locations anddetermined alleles. For example, a fragment including loci 421 of FIG. 4having allele T would be identified as being from Hap II. The secondgroup includes at least one nucleic acid molecule located at each of thefirst plurality of loci.

In step 1050, a computer system calculates a first value of the firstgroup of nucleic acid molecules. The first value defining a property ofthe nucleic acid molecules of the first group. Examples of the firstvalue include a tag count of the number of molecules in the first groupand a size distribution of the molecules in the first group.

In step 1060, the computer system calculates a second value of thesecond group of nucleic acid molecules. The second value defining aproperty of the nucleic acid molecules of the second group.

In step 1070, the first value is compared to the second value todetermine a classification of whether the first chromosomal regionexhibits a deletion or an amplification. A classification of a deletionor amplification existing can provide information about the organismhaving cells associated with cancer. Examples of a comparison includetaking a difference or ratio of the two values and comparing the resultto one or more threshold values, as is described herein. For example, aratio can be compared to the threshold values in an SPRT analysis.Example classifications can include positive (i.e. amplification ordeletion detected), negative, and unclassified, as well as varyingdegrees of positive and negative (e.g., using integer numbers between 1and 10, or real number between 0 and 1). The amplification can includesimple duplication. Such a method can detecting the presence ofcancer-associated nucleic acids, which include tumor DNA and DNA frompreneoplastic lesions, i.e. precurors of cancer.

E. Depth

The depth of analysis refers to the amount of molecules that need to beanalyzed to provide a classification or other determination within aspecified accuracy. In one embodiment, the depth may be calculated basedon a known aberration, and then a measurement and analysis with thatdepth may be performed. In another embodiment, the analysis may continueuntil a classification is made, and the depth at which theclassification is made can be used to determine a level of cancer (e.g.,a stage of the cancer or a size of a tumor). The following providesexamples of some calculations involving depth.

A deviation can refer to any difference or ratio as described herein. Asexample, the deviation can be between the first and second value or of aparameter from a threshold or the tumor concentration, as describedherein. If a deviation doubles, then the number of fragments that needto be measured decreases by ¼. More generally, if the deviationincreases by a factor of N, the number of fragments that need to bemeasured is 1/N². As a corollary, if the deviation decreases by 1/N,then the number of fragments to be tested increases by N². N can be areal number or an integer.

Suppose a case where the tumor DNA is 10% of the sample (e.g. plasma),and assume that a statistically significant difference is seen fromsequencing 10 million fragments. Then say for example, an enrichmentprocedure is performed so there is now 20% of tumoral DNA in the sample,then the number of fragments needed would be 2,500,000 fragments. Inthis manner, the depth can be correlated to the percentage of tumor DNAin the sample.

The amount of amplification will also affect the depth. For a regionthat has twice the amount of copies in that region (e.g. 4 as opposed tothe normal 2), suppose X number of fragments are required to beanalyzed. If a region has 4 times the amount of normal copies, then thisregion will require λ/4 amount of fragments.

F. Thresholds

The amount of a deviation of a parameter (e.g. difference or ratio ofvalues for each haplotype) from normal values can be used to provide adiagnosis, as is described above. For example, the deviation may be thedifference of the average size of the fragments from one haplotype of aregion to the average size of the fragments from the other haplotype. Ifthe deviation is above a certain amount (e.g., threshold as determinedfrom normal samples and/or regions), then a deletion or amplification isidentified. But, the extent above the threshold can be informative,which can lead to the use of multiple thresholds, each corresponding toa different level of cancer. For example, a higher deviation from normalcan provide what stage the cancer is at (e.g. stage 4 would have ahigher degree of imbalance than stage 3). A higher deviation can also bethe result of the tumor being large and thus releasing many fragments,and/or that the region is amplified many times.

In addition to providing different levels of cancer, varying thresholdscan also allow for efficient detection of regions with aberration or ofspecific regions. For example, one can set high thresholds to lookmainly for amplifications of three times and higher, which would give agreater imbalance than deletions of one haplotype. Deletions of twocopies of region can also be detected. Also, a lower threshold can beused to identify regions that might have an aberration, and then theseregions can be analyzed further to confirm whether an aberration doesexist and the location. For example, a binary search (or a search ofhigher order, such as an octree) can be performed, with lower levels inthe hierarchy using higher thresholds.

FIG. 11 shows a region 1110 with a subregion 1130 being deleted incancer cells along with measurements made in plasma to determine thedeleted region according to embodiments of the present invention. Thechromosomal region 1110 may be selected by any method mentioned herein,such as by splitting up a genome into equal sized segments. FIG. 11 alsoshows the number of allelic counts for each of the loci 1120. Acumulative total is also kept for region 1140 (normal region) and region1130 (deleted region), respectively.

If region 1110 is chosen for analysis, the number cumulative counts are258 for Hap I and 240 for Hap II providing a difference of 18 overeleven loci. Such a difference is smaller as a percentage of the totalnumber of counts, than just if the deleted subregion 1130 were analyzed.This makes sense as about half of region 1110 is normal, whereas all ofsubregion 1130 is deleted in the cancer cell. Thus, an aberration inregion 1110 could be missed depending on the threshold used.

To allow for detection of deletions of subregions, embodiments can use alower threshold for relatively large regions (for this example region1110 is assumed to be relatively large compared to the size of deletedregions to be identified). A lower threshold would identify moreregions, which could include some false positives, but it would reducethe false negatives. Now, the false positives could be removed throughfurther analysis, which can also pinpoint the aberration.

Once a region has been flagged for further analysis, the region can bedivided into subregions for further analysis. In FIG. 11, one can splitthe eleven loci in half (e.g. using a binary tree) to provide subregions1140 of six loci and subregion 1130 with five loci. These regions couldbe analyzed with a same threshold value or a more stringent thresholdvalue. In this example, subregion 1140 would then be identified as beingnormal and subregion 1130 identified as including a deletion oramplification. In this manner, larger regions can be dismissed as havingno aberration, and time can be spent further analyzing suspected regions(regions above a lower threshold) to identify subregions that show anaberration with high confidence (e.g. using a higher threshold).Although RHDO was used here, size techniques are equally applicable.

The size of the regions for the first level of search (and size ofsubregions of lower levels in the tree) can be chosen based on the sizeof aberrations to be detected. Cancers have been found to show tenregions with aberrations of 10 MB length. Patients have also had 100 MBregions exhibiting aberration. Later stages of cancer may have longersections of aberration.

G. Refinement of Location of Aberration within a Region

In the last section, division of a region into subregions based on atree search was discussed. Here, we discuss other methods for analyzingsubregions, and to pinpoint the aberration within a region.

FIG. 12 shows how the location of the aberrations can be mapped usingRHDO analysis according to embodiments of the present invention. Thechromosomal region is shown horizontally, with the haplotypes of thenon-cancer cells labeled Hap I and Hap II. The deleted region of Hap IIin the cancer cells is labeled as LOH.

As shown, RHDO analysis is started from the left side to the right sideof the hypothetical chromosome region 1202. Each of the arrowsrepresents a RHDO classification segment. Each segment can be consideredits own region, specifically a subregion with a subset of the hets ofthe larger region. The size of a RHDO classification segment isdependent on the number of loci (and positions of the loci) before aclassification can be determined. The number of loci included in eachRHDO segment is dependent on the number of molecules analyzed for eachsegment, the desired accuracy (e.g. the odds ratio in SPRT analysis),and the fractional concentration of tumor-derived DNA in the sample. Aclassification would be made when the number of molecules is adequate todetermine that a statistically significant difference is present betweenthe two haplotypes as in the example illustrated in FIG. 4 and FIG. 5.

Each of the solid horizontal arrows represents a RHDO classificationsegment showing that haplotype dosage imbalance is absent in the DNAsample. Within the region without a LOH in the tumor, six RHDOclassifications are made and each indicates the absence of haplotypedosage imbalance. The next RHDO classification segment 1210 crosses thejunction 1205 between the regions with and without LOH. In the lowerpart of FIG. 12, the SPRT curve for RHDO segment 1210 is shown. Theblack vertical arrow indicates the junction between the regions with andwithout LOH. With the accumulation of increasing data from the regionwith LOH, the RHDO classification of this segment indicates the presenceof haplotype dosage imbalance.

Each of the white horizontal arrows represents a RHDO classificationsegment that indicates the presence of haplotype dosage imbalance. Thesubsequent four RHDO on the right side also indicate the presence ofhaplotype dosage imbalance in the DNA sample. The location of thejunction between regions with and without LOH can be deduced to bewithin the first RHDO segment that show a change in the RHDOclassification, namely from the presence to absence of haplotype dosageimbalance or vice versa.

FIG. 13 shows a RHDO classification started from another directionaccording to embodiments of the present invention. In FIG. 13, RHDOclassifications from both directions are shown. From the RHDO analysisstarting from the left side, the junction between the regions with andwithout LOH can be deduced to be within the first RHDO segment 1310showing the presence of haplotype dosage imbalance. From the RHDOanalysis starting from the right side, the junction can be deduced to bewithin the first RHDO segment 1320 that indicates the absence ofhaplotype dosage imbalance. Combining the information from the RHDOanalysis conducted in the two directions, the location 1330 of thejunction between the regions with and without LOH can be deduced.

IV. Non-Specific Haplotype Detection of Aberrations

The RHDO method relies on using heterozygous loci. Now, the chromosomesof a diploid organism will have some differences, resulting in twohaplotypes, but the number of heterozygous loci can vary. Someindividuals may have relatively few heterozygous loci. The embodimentdescribed in this section can also be used for loci that are homozygousby comparing two regions and not two haplotypes of the same region.Thus, more data points may be obtained, although some drawbacks mayexist from the comparison to two different chromosomal regions.

In a relative chromosomal region dosage method, the number of fragmentsfrom one chromosomal region (e.g., as determined by counting thesequenced tags aligned to that region) is compared to an expected value(which may be from a reference chromosome region or from the same regionin another sample that is known to be healthy). In this manner, afragment would be counted for a chromosomal region regardless of whichhaplotype the sequenced tag is from. Thus, sequenced tags that containno hets could still be used. To perform the comparison, an embodimentcan normalize the tag count before the comparison. Each region isdefined by at least two loci (which are separated from each other), andfragments at these loci can be used to obtain a collective value aboutthe region.

A normalized value for the sequenced reads (tags) for a particularregion can be calculated by dividing the number of sequenced readsaligning to that region by the total number of sequenced reads alignableto the whole genome. This normalized tag count allows results from onesample to be compared to the results of another sample. For example, thenormalized value can be the proportion (e.g., percentage or fraction) ofsequenced reads expected to be from the particular region, as is statedabove. But, many other normalizations are possible, as would be apparentto one skilled in the art. For example, one can normalize by dividingthe number of counts for one region by the number of counts for areference region (in the case above, the reference region is just thewhole genome). This normalized tag count can then be compared against athreshold value, which may be determined from one or more referencesamples not exhibiting cancer.

The normalized tag count of the tested case would then be compared withthe normalized tag count of one or more reference subjects, e.g. thosewithout cancer. In one embodiment, the comparison is made by calculatingthe z-score of the case for the particular chromosomal region. Thez-score is calculated using the following equation: z-score=(normalizedtag count of the case−mean)/S.D., where “mean” is the mean normalizedtag count aligning to the particular chromosomal region for thereference samples; and S.D. is the standard deviation of the number ofnormalized tag count aligning to the particular region for the referencesamples. Hence, the z-score is the number of standard deviation that thenormalized tag count of a chromosomal region for the tested case is awayfrom the mean normalized tag count for the same chromosomal region ofthe one or more reference subjects.

In the situation when the tested organism has a cancer, the chromosomalregions that are amplified in the tumor tissues would beover-represented in the plasma DNA. This would result in a positivevalue of the z-score. On the other hand, chromosomal regions that aredeleted in the tumor tissues would be under-represented in the plasmaDNA. This would result in a negative value of the z-score. The magnitudeof the z-score is determined by several factors.

One factor is the fractional concentration of tumor-derived DNA in thebiological sample (e.g. plasma). The higher the fractional concentrationof tumor-derived DNA in the sample (e.g. plasma), the larger thedifference between the normalized tag count of the tested case and thereference cases would be. Hence, a larger magnitude of the z-score wouldresult.

Another factor is the variation of the normalized tag count in the oneor more reference cases. With the same degree of the over-representationof the chromosomal region in the biological sample (e.g. plasma) of thetested case, a smaller variation (i.e. a smaller standard deviation) ofthe normalized tag count in the reference group would result in a higherz-score. Similarly, with the same degree of under-representation of thechromosomal region in the biological sample (e.g. plasma) of the testedcase, a smaller standard deviation of the normalized tag count in thereference group would result in a more negative z-score.

Another factor is the magnitude of chromosomal aberration in the tumortissues. The magnitude of chromosomal aberration refers to the copynumber changes for the particular chromosomal region (either gain orloss). The higher the copy number changes in the tumor tissues, thedegree of over- or under-representation of the particular chromosomalregion in the plasma DNA would be higher. For example, the loss of bothcopies of the chromosome would result in greater under-representation ofthe chromosomal region in the plasma DNA than the loss of one of the twocopies of the chromosome and, hence, resulted in a more negativez-score. Typically, there are multiple chromosomal aberrations incancers. The chromosomal aberrations in each cancer can further vary byits nature (i.e. amplification or deletion), its degree (single ormultiple copy gain or loss) and its extent (size of the aberration interms of chromosomal length).

The precision of measuring the normalized tag count is affected by thenumber of molecules analyzed. We expect that 15,000, 60,000 and 240,000molecules would need to be analyzed to detect chromosomal aberrationswith one copy change (either gain or loss) when the fractionalconcentration is approximately 12.5%, 6.3% and 3.2% respectively.Further details of the tag counting for detection of cancer fordifferent chromosomal regions is described in U.S. Patent PublicationNo. 2009/0029377 entitled “Diagnosing Fetal Chromosomal Aneuploidy UsingMassively Parallel Genomic Sequencing” by Lo et al., the entire contentsof which are herein incorporated by reference for all purposes.

Embodiments can also use size analysis, instead of the tag countingmethod. Size analysis may also be used, instead of a normalized tagcount. The size analysis can use various parameters, as mentionedherein, and in U.S. patent application Ser. No. 12/940,992. For example,the Q or F values from above may be used. Such size values do not need anormalization by counts from other regions as these values do not scalewith the number of reads. Techniques of the haplotype-specific methodscan be used for the non-specific methods as well. For example,techniques involving the depth and refinement of a region may be used.In some embodiments, a GC bias for a particular region can be taken intoaccount when comparing two regions. Since the RHDO method uses the sameregion, such a correction is not needed.

V. Multiple Regions

Although certain cancers can typically present with aberrations inparticular chromosomal regions, such cancers do not always present inonly the same regions. For example, additional chromosomal regions couldshow aberrations, and the location of such additional regions may beunknown. Furthermore, when screening patients to identify early stagesof cancer, one may want to identify a broad range of cancers, whichcould show aberrations present throughout the genome. To address thesesituations, embodiments can analyze a plurality of regions in asystematic fashion to determine which regions show aberrations. Thenumber of aberrations and their location (e.g. whether they arecontiguous) can be used, for example, to confirm aberrations, determinea stage of the cancer, provide a diagnosis of cancer (e.g. if the numberis greater than a threshold value), and provide a prognosis based on thenumber and location of various regions exhibiting an aberration.

Accordingly, embodiments can identify whether an organism has cancerbased on the number of regions that show an aberration. Thus, one cantest a plurality of regions (e.g., 3000) to identify a number of regionsthat exhibit an aberration. The regions may cover the entire genome orjust parts of the genome, e.g., non-repeat region.

FIG. 14 is a flowchart of a method 1400 for analyzing a biologicalsample of an organism using a plurality of chromosomal regions accordingto embodiments of the present invention. The biological sample includesnucleic acid molecules (also called fragments).

In step 1410, a plurality of non-overlapping chromosomal regions of theorganism are identified. Each chromosomal region includes a plurality ofloci. As mentioned above, a region can be 1 Mb in size, or some otherequal-size. The entire genome can then include about 3,000 regions, eachof predetermined size and location. Also, as mentioned above, suchpredetermined regions can vary to accommodate a length of a particularchromosome or a specified number of regions to be used, and any othercriteria mentioned herein. If regions have different lengths, suchlengths can be used to normalize results, e.g., as described herein.

In step 1420, a location of the nucleic acid molecule in a referencegenome of the organism is identified for each of a plurality of nucleicacid molecules. The location may be determined in any of the waysmentioned herein, e.g., by sequencing the fragments to obtain sequencedtags and aligning the sequenced tags to the reference genome. Aparticular haplotype of a molecule can also be determined for thehaplotype-specific methods.

Steps 1430-1450 are performed for each of the chromosomal regions. Instep 1430, a respective group of nucleic acid molecules is identified asbeing from the chromosomal region based on the identified locations. Therespective group includes at least one nucleic acid molecule located ateach of the plurality of loci of the chromosomal region. In oneembodiment, the group can be fragments that align to a particularhaplotype of the chromosomal region, e.g., as in the RHDO method above.In another embodiment, the group can be of any fragment that aligns tothe chromosomal region, as in the methods described in section IV.

In step 1440, a computer system calculates a respective value of therespective group of nucleic acid molecules. The respective value definesa property of the nucleic acid molecules of the respective group. Therespective value can be any of the values mentioned herein. For example,the value can be the number of fragments in the group or a statisticalvalue of a size distribution of the fragments in the group. Therespective value can also be a normalized value, e.g., a tag count ofthe region divided the total number of tag counts for the sample or thenumber of tag counts for a reference region. The respective value canalso be a difference or ratio from another value (e.g., in RHDO),thereby providing the property of a difference for the region.

In step 1450, the respective value is compared to a reference value todetermine a classification of whether the first chromosomal regionexhibits a deletion or an amplification. This reference value can be anythreshold or reference value described herein. For example, thereference value could be a threshold value determined for normalsamples. For RHDO, the respective value could be the difference or ratioof tag counts for the two haplotypes, and the reference value can be athreshold for determining that a statistically significant deviationexists. As another example, the reference value could be the tag countor size value for another haplotype or region, and the comparison caninclude taking a difference or ratio (or function of such) and thendetermining if the difference or ratio is greater than a thresholdvalue.

The reference value can vary based on the results of other regions. Forexample, if neighboring regions also show a deviation (although smallcompared to one threshold, e.g., a z-score of 3), then a lower thresholdcan be used. For example, if three consecutive regions are all above afirst threshold, then cancer may be more likely. Thus, this firstthreshold may be lower than another threshold that is required toidentify cancer from non-consecutive regions. Having three regions (ormore than three) having even a small deviation can have a low enoughprobability of a chance effect that the sensitivity and specificity canbe preserved.

In step 1460, an amount of chromosomal regions classified as exhibitinga deletion or amplification is determined. The chromosomal regions thatare counted can have restrictions. For example, only regions that arecontiguous with at least one other region may be counted (or contiguousregions can be required to be of a certain size, e.g., 4 or moreregions). For embodiments where the regions are not equal, the numbercan also account for the respective lengths (e.g., the number could be atotal length of the aberrant regions).

In step 1470, the amount is compared to an amount threshold value todetermine a classification of the sample. As examples, theclassification can be whether the organism has cancer, a stage of thecancer, and a prognosis of the cancer. In one embodiment, all aberrantregions are counted and a single threshold value is used regardless ofwhere the regions appear. In another embodiment, a threshold value canvary based on the locations and size of the regions that are counted.For example, the amount of regions on a particular chromosome or arm ofa chromosome may be compared to a threshold for that particularchromosome (or arm). Multiple thresholds may be used. For instance, theamount of aberrant regions on a particular chromosome (or arm) must begreater than a first threshold value, and the total amount of aberrantregions in the genome must be greater than a second threshold value.

This threshold value for the amount of regions can also depend on howstrong the imbalance is for the regions counted. For example, the amountof regions that are used as the threshold for determining aclassification of cancer can depend on the specificity and sensitivity(aberrant threshold) used to detect an aberration in each region. Forexample, if the aberrant threshold is low (e.g. z-score of 2), then theamount threshold may be selected to be high (e.g., 150). But, if theaberrant threshold is high (e.g., a z-score of 3), then the amountthreshold may be lower (e.g., 50). The amount of regions showing anaberration can also be a weighted value, e.g., one region that shows ahigh imbalance can be weighted higher than a region that just shows alittle imbalance (i.e. there are more classifications than just positiveand negative for the aberration).

Accordingly, the amount (which may include number and/or size) ofchromosomal regions showing significant over- or under-representation ofa normalized tag count (or other respective value for the property ofthe group) can be used for reflecting the severity of disease. Theamount of chromosomal regions with an aberrant normalized tag count canbe determined by two factors, namely the number (or size) of chromosomalaberrations in the tumor tissues and the fractional concentration oftumor-derived DNA in the biological sample (e.g. plasma). More advancedcancers tend to exhibit more (and larger) chromosomal aberrations.Hence, more cancer-associated chromosomal aberrations would potentiallybe detectable in the sample (e.g. plasma). In patients with moreadvanced cancer, the higher tumor load would lead to a higher fractionalconcentration of tumor-derived DNA in the plasma. As a result, thetumor-associated chromosomal aberrations would be more easily detectedin the plasma sample.

In the context of cancer screening or detection, the amount ofchromosomal regions exhibiting over- or under-representation ofnormalized tag count (or other value) can be used to determine thepossibility of the tested subject of having cancer. Using a cutoff of ±2(i.e. z-score>2 or <−2), approximately 5% of the tested regions would beexpected to give a z-score significantly deviating from the mean of thecontrol subjects due to chance alone. When the whole genome is dividedinto 1 Mb segments, there would be approximately 3,000 segments for thewhole genome. Thus, approximately 150 segments would be expected to havea z-score of >2 or <−2.

Thus, a cutoff (threshold) value of 150 for the number of segments withz-score>2 or <−2 can be used to determine if a cancer is present. Othercutoff values for the number of segments with aberration z-score (e.g.,100, 125, 175, 200, 250 and 300) can be chosen to fit the diagnosticpurpose. A lower cutoff value, e.g. 100, would result in a moresensitive test but lower specificity and a higher cutoff value would bemore specific but less sensitive. The number of false-positiveclassifications can be reduced by increasing the cutoff values of thez-score. For example, if the cutoff value is increased to 3, then only0.3% of the segments would be falsely positive. In this situation, morethan 3 segments with aberrant z-score can be used to indicate thepresence of cancer. Other cutoff values can also be chosen, e.g. 1, 2,4, 5, 10, 20 and 30, to fit different diagnostic purposes. However, thesensitivity of detecting the cancer-associated chromosomal aberrationswould decrease with increasing the number of aberrant segments requiredfor making a diagnosis.

One possible approach for improving the sensitivity without sacrificingthe specificity is to take into account the result of the adjacentchromosomal segment. In one embodiment, the cutoff for the z-scoreremains to be >2 and <−2. However, a chromosomal region would beclassified as potentially aberrant only when two consecutive segmentswould show the same type of aberrations, e.g. both segments have az-score of >2. If the deviation of normalized tag count is a randomerror, the probability of having two consecutive segments being falselypositive in the same direction would be 0.125% (5%×5%/2). On the otherhand, if a chromosomal aberration encompasses two consecutive segments,the lower cutoff value would make the detection of the over- orunder-representation of the segments in the plasma sample moresensitive. As the deviation of the normalized tag count (or other value)from the mean of the control subjects is not due to random error, theconsecutive classification requirement would not have significantadverse effect on the sensitivity. In other embodiments, the z-score ofneighboring segments can be added together using a higher cutoff value.For example, the z-scores of three consecutive segments can be summedand a cutoff value of 5 can be used. This concept can be extended tomore than three consecutive segments.

The combination of amount and aberrant thresholds can also depend on thepurpose of the analysis, and any prior knowledge of the organism (orlack thereof). For example, if screening a normal healthy population forcancer, then one would typically use high specificity, potentially inboth the amount of regions (i.e. high threshold for the number ofregions) and an aberrant threshold for when a region is identified ashaving an aberration. But, in a patient with higher risk (e.g. a patientcomplaining of a lump or family history, smoker, HPV virus, hepatitisvirus, or other viruses) then the thresholds could be lower in order tohave more sensitivity (less false negatives).

In one embodiment, if one uses a 1-Mb resolution and a lower detectionlimit of 6.3% of tumor-derived DNA for detecting a chromosomalaberration, the number of molecules in each 1-Mb segment would need tobe 60,000. This would be translated to approximately 180 million (60,000reads/Mb×3,000 Mb) alignable reads for the whole genome.

FIG. 15 shows a table 1500 illustrating the depth required for variousnumbers of segments and fractional concentration of tumor-derivedfragments according to embodiments of the present invention. Column 1510provides the concentration of fragments from tumor cells for the sample.The higher the concentration, the easier to detect the aberration, soless number of molecules are required to be analyzed. Column 1520provides the estimated number of molecules required per segment, whichmay be calculated via the method described in the section above ondepth.

A smaller segment size would give a higher resolution for detectingsmaller chromosomal aberrations. However, this would increase therequirement of the number of molecules to be analyzed in total. A largersegment size would reduce the number of molecules required for theanalysis in the expense of resolution. Therefore, only largeraberrations can be detected. In one implementation, larger regions couldbe used, segments showing an aberration could be subdivided and thesesubregions analyzed to obtain better resolution (e.g., as is describedabove). Column 1530 provides the size of each segment. The smaller thevalue, the more regions are used. Column 1540 shows the number ofmolecules to be analyzed for the whole genome. Accordingly, if one hasan estimate (or minimum concentration to detect), the number ofmolecules to analyze can be determined.

VI. Progress Over Time

As a tumor progresses, the amount of tumor fragments will increase,since the tumor will release more DNA fragments (e.g., due to the growthof the tumor, more necrosis, or higher vascularity). The more DNAfragments from the tumor tissues into the plasma will increase thedegree of imbalance in the plasma (e.g., the difference in the tagcounts between the two haplotypes in RHDO will increase). Additionally,since the number of tumor fragments increases, the number of regionswhere aberration exists can more easily be detected. For example, theamount of tumor DNA for a region may be so small that the aberrationcannot be detected since a statistically significant difference cannotbe established because not enough fragments are analyzed when a tumor issmall and is releasing a small amount of cancer DNA fragments. Morefragments could be analyzed even when a tumor is small, but that mayrequire a large sample (e.g. a lot of plasma).

The tracking of the progress of a cancer can use the amount ofaberration in one or more regions (e.g., by imbalance or required depth)or the amount (number and/or size) of chromosomal regions exhibiting anaberration. In one example, if the amount of aberration of one region(or several regions) increases faster than the aberrations of otherregions, then that region(s) can be used as a preferred marker tomonitor the cancer. This increase could be the result of the tumor beinglarge and thus releasing many fragments, and/or that the region isamplified many times. One can also monitor the aberration value (e.g.the amount of aberration or the number of regions showing aberrations,or a combination thereof) after surgery to confirm that the tumor hasbeen properly removed.

In various implementations of the technology, the determination of thefractional concentration of tumoral DNA is used for the staging,prognostication, or monitoring the progress of the cancer. The measuredprogress can provide information as to the current stage of the cancerand how fast the cancer is growing or spreading. The “stage” of a canceris related to all or some of the following: the size of the tumor, thehistological appearance, the presence/absence of lymph node involvement,and the presence/absence of distant metastases. The “prognostication” ofcancer involves estimating the chance of disease progression and/or thechance of survival from the cancer. It can also involve an estimation ofthe time in which the patient would be free of clinical progression, orthe duration of survival. The “monitoring” of cancer would involvechecking to see if the cancer has progressed (e.g. has increased insize, has increased involvement of the lymph nodes, or has spread todistant organs, i.e. metastases). Monitoring can also involve thechecking if the tumor has been controlled by a treatment. For example,if treatment is effective, then one could see a reduction in the size ofa tumor, the regression of the metastases or lymph node involvement, animprovement in the general well-being of the patient (e.g. increase inbody weight).

A. Determination of the Fractional Concentration of Cancer DNA

One way of tracking an amount of increase in aberration for one or moreregions is to determine the fractional concentration of cancer DNA forthe regions(s). The change in the fractional concentration of cancer DNAcan then be used to track the tumor over time. This tracking can be usedto diagnose, e.g., a first measurement can provide the background level(which may correspond to a person's general level of aberration) andlater measurements can see changes, which would suggest a tumor growing(thus cancer). The changes in the fractional concentration of cancer DNAcan also be used to prognosticate how well a treatment is doing. Inother implementations of the technology, an increase in the fractionalconcentration of tumoral DNA in plasma would indicate a worse prognosisor an increase in the tumor load with the patient.

The fractional concentration of cancer DNA can be determined in variousways. For example, the difference in tag counts from one haplotypecompared to another (or one region compared to another). Another methodis the depth (i.e. the number of fragments analyzed) before astatistically significant difference is seen. For the earlier example,the difference in the haplotype dosage can be used for determining thefractional concentration of the tumor-derived DNA in the biologicalsample (e.g. plasma) by analyzing the chromosomal regions with loss ofheterozygozity.

It has been shown that the amount of tumor-derived DNA is positivelycorrelated with the tumor load in cancer patients (Lo et al. Cancer Res.1999; 59:5452-5. and Chan et al. Clin Chem. 2005; 51:2192-5). Therefore,the serial monitoring of the fractional concentration of tumor-derivedDNA in the biological samples (e.g. plasma samples) by RHDO analysis canbe used to monitor the disease progression of the patient. For example,the monitoring of the fractional concentration of the tumor-derived DNAin serially collected samples (e.g. plasma) after treatment can be usedfor determining a success of the treatment.

FIG. 16 shows a principle of measuring the fractional concentration oftumor-derived DNA in plasma by RHDO analysis according to embodiments ofthe present invention. An imbalance is determined between two haplotypesand the degree of imbalance can be used for determining the fractionalconcentration of tumoral DNA in the sample.

Hap I and Hap II represents the two haplotypes in the non-tumor tissues.Hap II is partially deleted in the tumor tissues in subregion 1610.Therefore, the Hap II-associated fragments corresponding to the deletedregion 1610 that are detected in plasma are contributed by the non-tumortissues. On the other hand, region 1610 in Hap I is present in bothtumor and non-tumor tissues. Therefore the difference between the readcounts of Hap I and Hap II would represent the amount of tumor-derivedDNA in plasma.

The fractional concentration of tumor-derived DNA (F) can be calculatedfrom the number of sequenced reads (tags) from the deleted andnon-deleted chromosomes for the chromosomal regions affected by LOHusing the following formula: F=(N_(HapI)−N_(HapII))/N_(HapI)×100%, whereN_(HapI) is the number of sequenced reads corresponding to alleles onHap I for the heterozygous SNPs located in the chromosomal regionsaffected by LOH; and N_(HapII) is the number of sequenced readscorresponding to alleles on Hap II for the heterozygous SNPs located inthe chromosomal region 1610 affected by LOH.

The above formula is equivalent to defining p as the cumulative tagcounts for heterozygous loci located on the chromosome region notincluding a deletion (Hap I) and q as the cumulative tag counts for thechromosomal region including a deletion (Hap II) 1610, with thefractional concentration of tumoral DNA in the sample (F) calculated asF=1−q/p. For the example illustrated in FIG. 11, the fractionalconcentration of tumoral DNA is 14% (1−104/121).

The fractional concentration of tumor-derived DNA in the plasma samplesof an HCC patient was collected before and after tumor resection. Beforetumor resection, N_(HapI) for a first haplotype of a given chromosomalregion was 30,443, and for N_(HapII) for a second haplotype of thechromosomal region was 16,221, which gives an F of 46.7%. After tumorresection, N_(HapI) was 31,534, and for N_(HapII) was 31,089, whichgives an F of 1.4%. This monitoring shows that the tumor resection wassuccessful.

The degree of the change in the circulating DNA size profile can also beused for determining the fractional concentration. In oneimplementation, the exact size distribution of plasma DNA derived fromboth tumor and non-tumor tissues can be determined, and then themeasured size distribution falling between the two known distributionscan provide the fractional concentration (e.g. using a linear modelbetween the two statistical values of the size distributions of thetumor and non-tumor tissues). Alternatively, a serial monitoring of sizechanges can be used. In one aspect, the change in size distribution isdetermined as being proportional to the fractional concentration oftumoral DNA in plasma.

The difference between different regions can also be used in a similarmanner, i.e., the non-specific haplotype detection methods describedabove. In tag counting methods, several parameters can be used for themonitoring of disease progression. For example, the magnitude of thez-score for regions exhibiting chromosomal aberrations can be used toreflect the fractional concentration of tumor-derived DNA in abiological sample (e.g. plasma). The degree of over- orunder-representation of a particular region is proportional to thefractional concentration of tumor-derived DNA in the sample and theextent or number of copy number change in the tumor tissues. Themagnitude of the z-score is a measurement of the degree of over- orunder-representation of the particular chromosomal region in the samplecompared to the control subjects. Therefore, the magnitude of thez-score can reflect the fractional concentration of tumor DNA in thesample and, hence, the tumor load of the patient.

B. Tracking Number of Regions

As mentioned above, the number of regions exhibiting chromosomalaberration can be used to screen for cancer, and it can be used tomonitor and prognosticate as well. As examples, the monitoring can beused to determine a current stage of the cancer, if cancer hasreappeared, and if treatment has worked. As a tumor progresses, thegenomic makeup of the tumor will degrade more. To identify thiscontinued degradation, the methods of tracking a number of region (e.g.,predefined regions of 1 Mb) can be used to identify the progression of atumor. Tumors at more advanced stages of cancer would then have moreregions that exhibit an aberration.

C. Method

FIG. 17 is a flowchart illustrating a method of determining a progressof chromosomal aberrations in an organism using biological samplesincluding nucleic acid molecules according to embodiments of the presentinvention. In one embodiment, at least some of the nucleic acidmolecules are cell-free. As examples, the chromosomal aberrations may befrom malignant tumors or premalignant lesions. Also, an increase inaberrations could be due to the organism having more and more cellscontaining chromosomal aberrations over time, or due to the organismhaving a proportion of cells that contain an increasing amount ofaberrations per cell. As an example of a decrease, treatment (e.g.surgery or chemotherapy) can cause the removal or reduction of cellsassociated with cancer.

In step 1710, one or more non-overlapping chromosomal regions of theorganism are identified. Each chromosomal region including a pluralityof loci. The regions can be identified by any suitable method, e.g.,those described herein.

Steps 1720-1750 are performed for each of a plurality of times. Eachtime corresponds to a different time when a sample was obtained from theorganism. The current sample is the sample being analyzed for a giventime period. For example, a sample may be taken every month for 6months, and the analysis can be made soon after the sample was obtained.Alternatively, the analysis can be made after several measurements aretaken over a san of several time periods.

In step 1720, a current biological sample of the organism is analyzed toidentify a location of the nucleic acid molecule in a reference genomeof the organism. The location may be determined in any of the waysmentioned herein, e.g., by sequencing the fragments to obtain sequencedtags and aligning the sequenced tags to the reference genome. Aparticular haplotype of a molecule can also be determined for thehaplotype-specific methods.

Steps 1730-1750 are performed for each of the one or more chromosomalregions. When a plurality of regions are used, embodiments from sectionV may be used. In step 1730, a respective group of nucleic acidmolecules is identified as being from the chromosomal region based onthe identified locations. The respective group includes at least onenucleic acid molecule located at each of the plurality of loci of thechromosomal region. In one embodiment, the group can be fragments thatalign to a particular haplotype of the chromosomal region, e.g., as inthe RHDO method above. In another embodiment, the group can be of anyfragment that aligns to the chromosomal region, as in the methodsdescribed in section IV.

In step 1740, a computer system calculates a respective value of therespective group of nucleic acid molecules. The respective value definesa property of the nucleic acid molecules of the respective group. Therespective value can be any of the values mentioned herein. For example,the value can be the number of fragments in the group or a statisticalvalue of a size distribution of the fragments in the group. Therespective value can also be a normalized value, e.g., a tag count ofthe region divided the total number of tag counts for the sample or thenumber of tag counts for a reference region. The respective value canalso be a difference or ratio from another value (e.g., in RHDO),thereby providing the property of a difference for the region.

In step 1750, the respective value is compared to a reference value todetermine a classification of whether the first chromosomal regionexhibits a deletion or an amplification. This reference value can be anythreshold or reference value described herein. For example, thereference value could be a threshold value determined for normalsamples. For RHDO, the respective value could be the difference or ratioof tag counts for the two haplotypes, and the reference value can be athreshold for determining that a statistically significant deviationexists. As another example, the reference value could be the tag countor size value for another haplotype or region, and the comparison caninclude taking a difference or ratio (or function of such) and thendetermining if the difference or ratio is greater than a thresholdvalue. The reference value can be determined according to any suitablemethod and criteria, e.g., as described herein.

In step 1760, the classifications of each of the chromosomal regions atthe plurality of times are used to determine the progress of thechromosomal aberrations in the organism. The progress can be used todetermine whether the organism has cancer, a stage of the cancer, and aprognosis of the cancer. Each of these determination can involve acancer classification, as is described herein.

This cancer classification can be performed in various ways. Forexample, an amount of aberrant regions can be counted and compared to athreshold. The classification for the regions can be a numerical value(e.g., a tumor concentration, with the respective and reference valuesbeing values for difference haplotypes or different regions) and thechange in the concentration can be determined. The change inconcentration can be compared to a threshold to determine that asignificant increase has occurred, thereby signaling the existence of atumor.

VII. Examples A. RHDO Using SPRT

In this section, we show an example of using relative haplotype dosage(RHDO) analysis using SPRT for a hepatocellular carcinoma (HCC) patient.In the tumor tissue of this patient, deletion of one of the twochromosome 4 was observed. This results in a loss of heterozygozity forthe SNPs on chromosome 4. For the haplotyping of this patient, thegenomic DNA for the patient, his wife and his son was analyzed and thegenotypes of the three individuals were determined. The constitutionalhaplotypes of the patient was then derived from their genotypes.Massively parallel sequencing was performed and the sequenced reads withSNP alleles corresponding to the two haplotypes of chromosome 4 wereidentified and counted.

The equations and principles of RHDO and SPRT has been described above.In one embodiment, the RHDO analysis would be programmed to detect, forexample 10% of difference in the haplotype dosages in the DNA samplewhich is corresponding to the presence of 10% of tumor-derived DNA whenone of the two haplotypes is amplified or deleted. In other embodiments,the sensitivity of RHDO analysis can be set to detect 2%, 5%, 15%, 20%,25%, 30%, 40% and 50%, etc, of tumor-derived DNA in the DNA sample. Thesensitivity of the RHDO analysis can be adjusted in the parameter forthe calculation of the upper and lower threshold of the SPRTclassification curves. The adjustable parameters can be the desiredlevel of detection limit (e.g. what percentage of tumor concentrationshould be detectable, which affects the number of molecule analyzed) andthe threshold for the classification, e.g., using an odds ratio (ratioof tag counts for one haplotype relative to the tag counts for otherhaplotype).

In this RHDO analysis, the null hypothesis is that the two haplotypesfor chromosome 4 are present in the same dosage. The alternativehypothesis is that the dosages of the two haplotypes differ by more than10% in the biological sample (e.g. plasma). The numbers of sequencedreads with SNP alleles corresponding to the two haplotypes were comparedstatistically against the two hypotheses as the data from different SNPsaccumulate. A SPRT classification is made when the accumulated data aresufficient to determine if the two haplotype dosages are present inequal amount or differ by at least 10% statistically. A typical SPRTclassification block on the q arm of chromosome 4 is shown in FIG. 18A.The threshold of 10% is used here for illustration purpose only. Otherdegrees of difference (e.g 0.1%, 1%, 2%, 5%, 15% or 20%) can also bedetected. In general, the lower the degree of difference that one wouldlike to detect, the more DNA molecules one would need to analyze.Conversely, the larger the degree of difference that one would like todetect, the smaller the number of DNA molecules that one would need toanalyze and yet to achieve a statistically significant result. For thisanalysis, an odds ratio is used for SPRT, but other parameters such as az-score or a p-value may be used.

In this plasma sample of the HCC patient taken at the time of diagnosis,there were 76 and 148 successful RHDO classifications for the p and qarms of chromosome 4, respectively. All the RHDO classificationsindicate that there was haplotype dosage imbalance in the plasma sampletaken at the time of diagnosis. As a comparison, the plasma sample ofthe patient taken after surgical resection of the tumor was alsoanalyzed, as shown in FIG. 18B. For the post-treatment samples, therewere 4 and 9 successful RHDO classifications for the p and q arms ofchromosome 4, respectively. All the four RHDO classifications indicatethat there was no observable haplotype dosage imbalance of >10% in theplasma sample. Among the 9 RHDO classifications for chromosome 4q, 7indicate the absence of haplotype dosage imbalance and 2 indicate thatimbalance is present. The number of RHDO blocks showing dosage imbalanceof >10% has significantly reduced after the tumor resection indicatingthat the size of the chromosomal region showing dosage imbalance of >10%is significantly smaller in the post-treatment sample than in thepre-treatment sample. These results suggest that the fractionalconcentration of tumor DNA in the plasma has reduced after the surgicalresection of the tumor.

When compared with the non-haplotype specific methods, RHDO analysiscould provide a more precise estimation of the fractional concentrationof the tumoral DNA and is particularly useful for the monitoring ofdisease progression. Thus, one would expect that cases with a diseaseprogression would exhibit an increase in the fractional concentration oftumoral DNA in plasma; while cases with stable disease or those in whichthe tumor has regressed or reduced in size would have a reduction in thefractional concentration of tumoral DNA in plasma.

B. Targeted Analysis

In selected embodiments, the universal sequencing of DNA fragments canbe performed following a target enrichment approach. Such an approach isherein also referred to as enriched target sequencing. One embodiment ofsuch an approach is the prior selection of fragments using anin-solution capture system (e.g. the Agilent SureSelect system, theIllumina TruSeq Custom Enrichment Kit(illumina.com/applications/sequencing/targeted_resequencing.ilmn), or bythe MyGenostics GenCap Custom Enrichment system (mygenostics.com/)) or amicroarray-based capture system (e.g. the Roche NimbleGene system).Although some other regions can be captured, certain regions arepreferentially captured. Such methods can allow such regions to beanalyzed at more depth (e.g., more fragments can be sequenced oranalyzed with digital PCR), and/or at lower cost. The greater depth canincrease the sensitivity in the region. Other enrichment methods can beperformed based on size of fragments and methylation patterns.

Accordingly, an alternative to analyzing the DNA sample in a genomewidefashion is to target the regions of interest for detecting the commonchromosomal aberrations. The targeted approach can potentially improvethe cost-effectiveness of this approach because the analytical processwould mainly focus on the regions that chromosomal aberrations arepotentially present or regions with changes that would be particularlycharacteristic for a particular tumor type, or those with changes thatwould be particularly clinically important. Examples of the latterinclude changes that would occur early on in the oncogenesis of aparticular cancer type (e.g. the presence of amplifications of 1q and8q, and deletion of 8q are early chromosomal changes in HCC—vanMalenstein et al. Eur J Cancer 2011; 47:1789-97); or changes that areassociated with good or bad prognosis (e.g, gains at 6q and 17q, andloss at 6p and 9p are observed during tumor progression, and thepresence of LOH at 18q, 8p and 17p are associated with poorer survivalin colorectal cancer patients—Westra et al. Clin Colorectal Cancer 2004;4:252-9); or which are predictive of treatment response (e.g. thepresence of gains at the 7p is predictive of the response to tyrosinkinase inhibitors in patients with epidermal growth factor receptormutations—Yuan et al. J Clin Oncol 2011; 29:3435-42). Other examples ofgenomic regions altered in cancer can be found in a number of onlinedatabase (e.g. the Cancer Genome Anatomy Project database(cgap.nci.nih.gov/Chromosomes/RecurrentAberrataions) and the Atlas ofGenetics and Cytogenetics in Oncology and Haematology(atlasgeneticsoncology.org//Tumors/Tumorliste.html). In contrast, innon-targeted genomewide approach, regions that chromosomal aberrationsare unlikely to occur would be analyzed to the same degree as regionswith potential aberrations.

We have applied the target-enrichment strategy to analyze the plasmasamples from 3 HCC patients and 4 healthy control subjects. The targetenrichment was performed using the SureSelect capture system fromAgilent (Gnirke et al. Nat. Biotechnol 2009.27:182-9). The SureSelectsystem was chosen as an example of a possible target enrichmenttechnology. Other solution phase (the IlluminaTruSeq Custom Enrichmentsystem) or solid phase (e.g. the Roche-Nimblegen system) target capturesystems and amplicon-based target enrichment systems (e.g.QuantaLifesystem and RainDance system) can also be used. The capturingprobes are designed to be located on chromosomal regions that commonlyand uncommonly show aberrations in HCC. After target capturing, each DNAsample was then sequenced by one lane of a flow cell on an IlluminaGAIIxanalyzer. The regions that amplification and deletion uncommonly occurare used as reference to compare with the regions in whichamplifications and deletions are more commonly present.

In FIG. 19, common chromosomal aberrations found in HCC are shown(figure is adapted from Wong et al (Am J Pathol 1999; 154:37-43). Thelines on the right side of the chromosome ideogram represent chromosomalgains and lines on the left side represent chromosomal loss ofindividual patient samples. Thick lines represent high-level gains. Therectangles represent the locations of the target capturing probes.

Targeted Tag Counting Analysis

For the detection of chromosomal aberrations, we first calculated thenormalized tag counts for regions with potential aberrations and thereference regions. The normalized tag count was then corrected for GCcontent of the region as previously described by Chen et al (PLoS One2011; 6:e21791). In the current example, the p-arm of chromosome 8 waschosen as the region of potential aberration and the q-arm of chromosome9 was chosen as the reference region. The tumor tissues of the three HCCpatients were analyzed using the AffymetrixSNP 6.0 array for chromosomalaberrations. The changes in chromosome dosage for 8p and 9q in the tumortissues are shown below for the 3 patients. Patient HCC 013 had a lossfor 8p and no change for 9q. Patient HCC 027 had a gain for 8p and nochange for 9q. Patient HCC 023 had a loss for 8p and no change for 9q.

The ratio of the normalized tag count between chr 8p and 9q was thencalculated for the three HCC patients and four healthy control subjectsusing a targeted analysis. FIG. 20A shows the results normalized tagcounts ratio for the HCC and healthy patients. For cases HCC 013 andHCC023, a reduced normalized tag count ratio between 8p and 9q wasobserved. This is consistent with the finding of the loss of chromosome8p in the tumor tissues. For case HCC 027, an increased ratio isobserved and is consistent with the gain in chromosome 8p in the tumortissues of this case. The dotted lines represent the region with twostandard deviations of mean value of the four normal cases.

Targeted Size Analysis

In previous sections, we describe the principle of detectingcancer-associated alterations by determining the size profile of plasmaDNA fragments in cancer patients. Size alterations can also be detectedwith the target-enrichment approach. For the three HCC cases (HCC 013,HCC 027 and HCC023), the size of each sequenced DNA fragment wasdetermined after aligning the sequenced reads to the reference humangenome. The size of the sequenced DNA fragments was deduced from thecoordinates of the outer-most nucleotides of both ends. In otherembodiments, the whole DNA fragment will be sequenced and the fragmentsize can then be determined directly from the sequenced length. The sizedistribution of DNA fragments aligning to chromosome 8p was compared tothe size distribution of DNA fragments aligning to chromosome 9q. Forthe detection of the difference in the size distributions of the twopopulations of DNA, the proportion of DNA fragments shorter than 150 bpwas first determined for each of the population in the current example.In other embodiments, other size cutoff values, e.g. 80 bp, 110 bp, 100bp, 110 bp, 120 bp, 130 bp, 140 bp, 160 bp and 170 bp can be used. Thenthe ΔQ values were determined as the difference of the two proportions.ΔQ=Q_(8p)-Q_(9q), where Q_(8p) is the proportion of DNA fragmentsaligning to chromosome 8p that are shorter than 150 bp; and Q_(9q) isthe proportion of DNA fragments aligning to chromosome 9q that areshorter than 150 bp.

As a shorter size distribution of DNA fragments would give a highervalue of proportion of DNA shorter than the cutoff value (i.e. 150 bp inthe current example), a higher (more positive) value of ΔF wouldrepresent a shorter distribution of the DNA fragments aligning tochromosome 8p relative to those aligning to chromosome 9q. On thecontrary, a smaller (or more negative) result would indicate a longersize distribution of the DNA fragment aligning to chromosome 8p relativeto those aligning to chromosome 9q.

FIG. 20B shows the results of a size analysis after target enrichmentand massively parallel sequencing for the 3 HCC patients and 4 healthycontrol subjects. Positive values of ΔQ in the four healthy controlsubjects indicate a slightly shorter size distribution of DNA fragmentsaligning to the chromosome 8p compared with those aligning to chromosome9q. The dotted lines represent the interval of ΔQ within two standarddeviations from the mean for the four control subjects. The ΔQ values ofcases HCC 013 and HCC 023 were more than two standard deviations belowthe mean value of the control subjects. These two cases had deletion ofchromosome 8p in the tumor tissues. The deletion of 8p in the tumorwould result in reduced contribution of tumor-derived DNA to the plasmafor this chromosomal region. As the tumor derived DNA in the circulationare shorter than the DNA derived from non-tumor tissues, this would leadto an apparently longer size distribution for plasma DNA fragmentsaligning to chromosome 8p. This is consistent with a lower (morenegative) value of ΔQ in these two cases. In contrast, the amplificationof 8p in case HCC 027 would lead to an apparently shorter distributionfor DNA fragments aligning to this region. Thus, a higher proportion ofplasma DNA fragments aligning to 8p would be considered short. This isconsistent with the observation that the ΔQ value of HCC 027 is morepositive than the healthy control subjects.

C. Multiple Regions for Detection of Tumor-Derived ChromosomalAberrations

Chromosomal aberrations, including deletion and amplification of certainchromosomal regions, are commonly detected in tumor tissues.Characteristic patterns of chromosomal aberrations are observed indifferent types of cancers. Here, we use several examples to illustratethe different approaches for detecting these cancer-associatedchromosomal aberrations in the plasma of cancer patients. Our approachis also useful for the screening of cancer and the monitoring of diseaseprogression and response to treatment. Samples from one HCC patient andtwo nasopharyngeal (NPC) patients were analyzed. For the HCC patient,venous blood samples were collected before and after surgical resectionof the tumor. For the two NPC patients, venous blood samples werecollected at the time of diagnosis. Additionally, the plasma samples ofone chronic hepatitis B carrier and one subject with detectableEpstein-Barr virus DNA in the plasma were analyzed. These two subjectsdid not have any cancer.

A detection of tumor-derived chromosomal aberrations was performed withmicroarray analysis. Specifically, the DNA extracted from the bloodcells and the tumor sample of the HCC patient were analyzed using theAffymetrix SNP6.0 microarray system. The genotypes of the blood cellsand the tumor tissues were determined using the Affymetrix GenotypingConsole v4.0. Chromosomal aberrations, including gains and deletions,were determined using the Birdseed v2 algorithm based on the intensitiesof the different alleles of the SNPs and the copy number variation (CNV)probes on the microarray.

Count-Based Analysis

To perform sequenced tag counting analysis in plasma, ten milliliters ofvenous blood were collected from each of the subjects. For each bloodsample, plasma was isolated after centrifugation of the sample. DNA wasextracted from 4-6 mL of plasma using the QIAmp blood mini Kit (Qiagen).Plasma DNA library was constructed as previously described (Lo Y M D.Sci Transl Med 2010, 2:61ra91), and then subjected to massively parallelsequencing using the Illumina Genome Analyzer platform. Paired-endsequencing of the plasma DNA molecules was performed. Each molecule wassequenced at each of the two ends for 50 bp, thus totaling 100 bp permolecule. The two ends of each sequence were aligned to thenon-repeat-masked human genome (Hg18 NCBI.36 downloaded from UCSCgenome.ucsc.edu) using the SOAP2 program (soap.genomics.org.cn/) (Li Ret al. Bioinformatics 2009, 25:1966-7).

The genome was then divided into multiple 1-megabase (1-Mb) segments andthe number of sequenced reads aligning to each 1-Mb segment wasdetermined. The tag count of each bin was then corrected with analgorithm based on locally weighted scatterplot smoothing (LOESS)regression according to the GC content of each bin (Chen E et al. PLoSOne 2011, 6:e21791). This correction aims to minimize the quantitativebias related to sequencing which arises because of the difference in GCcontent between different genomic segments. The above-mentioned divisioninto 1-Mb segments is used for illustration purpose. Other segmentsizes, e.g. 2 Mb, 10 Mb, 25 Mb, or 50 Mb, etc, can also be used. It isalso possible to select the segment size based on the genomiccharacteristics of a particular tumor in a particular patient and aparticular type of tumor in general. Furthermore, if the sequencingprocess can be shown to have a low GC bias, for example, for singlemolecule sequencing techniques, such as the Helicos system(www.helicosbio.com) or the Pacific Biosciences Single MolecularReal-Time system (www.pacificbiosciences.com), the GC correction stepcan be omitted.

In a previous study, we have sequenced 57 plasma samples from subjectswithout cancer. These plasma sequencing results were used fordetermining the reference range of tag counts for each 1-Mb segment. Foreach 1-Mb segment, the mean and standard deviation of tag counts of the57 individuals were determined. Then, the results of the study subjectswere expressed as a z-score as calculated using the following equation:z-score=(no. of sequenced tag of the case−mean)/S.D, where “mean” is themean number of sequenced tags aligning to the particular 1-Mb segmentfor the reference samples; and S.D. is the standard deviation of thenumber of sequenced tags aligning to the particular 1-Mb segment for thereference samples.

FIGS. 21-24 show the results of the sequenced tag counting analysis ofthe four study subjects. The 1-MB segments are shown at the edge of theplots. Human chromosome numbers and ideograms (outermost ring) areoriented pter-qter in a clockwise direction (centromeres are shown inyellow). In FIG. 21, the inner ring 2101 shows regions of aberration(deletion or amplification) as determined from analyzing the tumor.Inner ring 2101 is shown with five scales. The scale is from −2 (mostinner line) to +2 (most outer line). The value of −2 represents the lossof both chromosome copies for the corresponding region. The value of −1represents the loss of one of the two chromosome copy. The value of 0represents no chromosome gain or loss. The value of +1 represents thegain of one chromosome copy and +2 represents the gain of two chromosomecopies.

The middle ring 2102 shows results from the analysis of plasma. As onecan see, the results mirror the inner ring. Middle ring 2102 is morelines of scale, but the progression is the same. The outer ring 2103shows data points from analyzing plasma after treatment, and these datapoints are grey (confirming no over/under representation—no aberration).

Chromosomal regions with over-representation of sequenced tags in plasma(z-score of >3) are represented by green dots 2110. Regions withunder-representation of sequenced tags in plasma (z-score of <−3) arerepresented by red dots 2120. Regions with no significant chromosomalaberration detected in plasma (z-score between −3 and 3) are representedby grey dots. The over/under representation is normalized by the totalnumber of counts. With amplification before sequencing, thenormalization may take into account GC bias.

FIG. 21 shows Circos plots of an HCC patient depicting data fromsequenced tag counting of plasma DNA according to embodiments of thepresent invention. Tracks from inside to outside: chromosomalaberrations of the tumor tissue detected by microarray analysis (red andgreen color represent deletion and amplification, respectively); z-scoreanalysis for a plasma sample obtained before surgical resection oftumor, and at 1 month after the resection. Before tumor resection, thechromosomal aberrations detected in the plasma correlate well with thoseidentified in the tumor tissue by the microarray analysis. After tumorresection, most cancer-associated chromosomal aberrations disappeared inthe plasma. These data reflect the value of such an approach formonitoring the disease progress and therapeutic efficacy.

FIG. 22 shows a sequenced tag counting analysis for the plasma sample ofa chronic HBV carrier without HCC according to embodiments of thepresent invention. In contrast to the HCC patient (FIG. 21),cancer-associated chromosomal aberrations were not detected in theplasma of this patient. These data reflect the value of the approach forcancer screening, diagnosis, and monitoring.

FIG. 23 shows a sequenced tag counting analysis for the plasma sample ofa patient with stage 3 NPC according to embodiments of the presentinvention. Chromosomal aberrations were detected in the plasma sampletaken before treatment. Specifically, significant aberrations wereidentified in chromosomes 1, 3, 7, 9, and 14.

FIG. 24 shows a sequenced tag counting analysis for the plasma sample ofa patient with stage 4 NPC according to embodiments of the presentinvention. Chromosomal aberrations were detected in the plasma sampletaken before treatment. When compared with the patient with Stage 3disease (FIG. 23), more chromosomal aberrations were detected. Thesequenced tag counts also deviated more from the mean of the controls,i.e. the z-score deviates more from zero (either positively ornegatively). The increased number of chromosomal aberrations and higherdegree of deviation of the sequenced tag counts compared with controlsare reflecting the more profound degree of genomic alterations in themore advanced stage of disease and hence reflect the value of such anapproach for staging, prognostication and monitoring of the cancer.

Size-Based Analysis

In previous studies, it has been shown that the size distribution of DNAderived from tumor tissues is shorter than the size distribution ofthose derived from non-tumor tissues (Diehl F et al. Proc Natl Acad SciUSA 2005, 102(45):16368-73). In the previous sections, we have outlinedthe approach for detecting the plasma haplotype imbalance by sizeanalysis of plasma DNA. Here, we used the sequencing data of the HCCpatient to further illustrate this approach.

For illustration purpose, we identified two regions for size analysis.In one region (chromosome 1 (chr1); coordinates: 159,935,347 to167,219,158), duplication of one of the two homologous chromosomes wasdetected in the tumor tissue. In the other region (chromosome 10(chr10); coordinates: 100,137,050 to 101,907,356), deletion of one ofthe two homologous chromosome (i.e. LOH) was detected in the tumortissue. In addition to determining which haplotype a sequenced fragmentcame from, the size of the sequenced fragment was also determinedbioinformatically using the coordinates of the outermost nucleotides ofthe sequenced fragment in the reference genome. Then, the sizedistributions of fragments from each of the two haplotypes weredetermined.

For the LOH region of Chr10, one haplotype was deleted in the tumortissue (the deleted haplotype). Therefore, all plasma DNA fragmentsaligning to this deleted haplotype were derived from the non-cancertissues. On the other hand, the fragments aligning to the haplotype thatwas not deleted in the tumor tissues (the non-deleted haplotype) can bederived from the tumor or the non-tumor tissues. As the sizedistribution of the tumor-derived DNA is shorter, we would expect ashorter size distribution for fragments from the non-deleted haplotypewhen compared with those from the deleted haplotype. The difference inthe two size distributions can be determined by plotting the cumulativefrequencies of fragments against the size of DNA fragments. Thepopulation of DNA with the shorter size distribution would have moreabundant short DNA and hence a more rapid increase in the cumulativefrequency at the short end of the size spectrum.

FIG. 25 shows a plot of cumulative frequency of plasma DNA against sizefor a region exhibiting LOH in the tumor tissue according to embodimentsof the present invention. The X-axis is the size of a fragment in basepairs. The Y-axis is the percentage of fragments have a size below thevalue on the X-axis. The sequences from the non-deleted haplotype hadmore rapid increase and higher cumulative frequency below the size of170 bp when compared with the sequences from the deleted haplotype. Thisindicates that short DNA fragments from the non-deleted haplotype weremore abundant. This is consistent with the prediction above because ofthe contribution of the short tumor-derived DNA from the non-deletedhaplotype.

In one embodiment, the difference in size distribution can be quantifiedby the difference in the cumulative frequencies of the two populationsof DNA molecules. We define ΔQ as the difference of the cumulativefrequencies of the two populations. ΔQ=Q_(non-deleted)−Q_(deleted),where Q_(non-deleted) represents the cumulative frequency for sequencedDNA fragments coming from the non-deleted haplotype; and Q_(deleted)represents the cumulative frequency for sequenced DNA fragments comingfrom the deleted haplotype.

FIG. 26 shows ΔQ against the size of sequenced plasma DNA for the LOHregion. ΔQ reaches 0.2 at the size of 130 bp according to embodiments ofthe present invention. This indicates that using 130 bp as a cutoff fordefining short DNA is optimal, for use in the equations above. Usingthis cutoff, short DNA molecules are 20% more abundant in the populationfrom the non-deleted haplotype when compared with the population fromthe deleted haplotype. This percentage difference (or similarly derivedvalue) can then be compared with a threshold value derived fromindividuals without cancer,

For the region with chromosomal amplification, one haplotype wasduplicated in the tumor tissue (the amplified haplotype). Because anextra amount of the short tumor-derived DNA molecules from thisamplified haplotype would be released into the plasma, the sizedistribution for the fragments from the amplified haplotype would beshorter than the size distribution for the fragments from thenon-amplified haplotype. Similar to the LOH scenario, the difference inthe size distributions can be determined by plotting the cumulativefrequencies of fragments against the size of DNA fragments. Thepopulation of DNA with the shorter size distribution would have moreabundant short DNA and hence a more rapid increase in the cumulativefrequency at the short end of the size spectrum.

FIG. 27 shows a plot of cumulative frequency of plasma DNA against sizefor a region with chromosome duplication in the tumor tissue accordingto embodiments of the present invention. The sequences from theamplified haplotype had more rapid increase and higher cumulativefrequency below the size of 170 bp when compared with the sequences fromthe non-amplified haplotype. This indicates that short DNA fragmentsfrom the amplified haplotype were more abundant. This is consistent withthe prediction shown below because a larger number of shorttumor-derived DNA were derived from the amplified haplotype.

Similar to the LOH scenario, the difference in size distribution can bequantified by the difference in the cumulative frequencies of the twopopulations of DNA molecules. We define ΔQ as the difference of thecumulative frequencies of the two populations.ΔQ=Q_(amplified)−Q_(non-amplified), where Q_(amplified) represents thecumulative frequency for sequenced DNA fragments coming from theamplified haplotype; and Q_(non-amplified) represents the cumulativefrequency for sequenced DNA fragments coming from the non-amplifiedhaplotype.

FIG. 28 shows ΔQ against the size of sequenced plasma DNA for theamplified region according to embodiments of the present invention. ΔQreached 0.08 at the size of 126 bp according to embodiments of thepresent invention. This indicates that using 126 bp as a cutoff fordefining short DNA, short DNA molecules are 8% more abundant in thepopulation from the amplified haplotype when compared with thepopulation from the non-amplified haplotype.

D. Additional Techniques

In other embodiments, sequence-specific techniques may be used. Forexample, oligonucleotides may be designed to hybridize to fragments of aparticular region. The oligonucleotides could then be counted, in asimilar fashion as the sequenced tag counts. This method may be used forcancers that exhibit particular aberrations.

VIII. Computer System

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 9 incomputer apparatus 900. In some embodiments, a computer system includesa single computer apparatus, where the subsystems can be the componentsof the computer apparatus. In other embodiments, a computer system caninclude multiple computer apparatuses, each being a subsystem, withinternal components.

The subsystems shown in FIG. 29 are interconnected via a system bus2975. Additional subsystems such as a printer 2974, keyboard 2978, fixeddisk 2979, monitor 2976, which is coupled to display adapter 2982, andothers are shown. Peripherals and input/output (I/O) devices, whichcouple to I/O controller 2971, can be connected to the computer systemby any number of means known in the art, such as serial port 2977. Forexample, serial port 2977 or external interface 2981 (e.g. Ethernet,Wi-Fi, etc.) can be used to connect computer system 2900 to a wide areanetwork such as the Internet, a mouse input device, or a scanner. Theinterconnection via system bus 2975 allows the central processor 2973 tocommunicate with each subsystem and to control the execution ofinstructions from system memory 2972 or the fixed disk 2979, as well asthe exchange of information between subsystems. The system memory 2972and/or the fixed disk 2979 may embody a computer readable medium. Any ofthe values mentioned herein can be output from one component to anothercomponent and can be output to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 2981 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

It should be understood that any of the embodiments of the presentinvention can be implemented in the form of control logic using hardwareand/or using computer software in a modular or integrated manner. Basedon the disclosure and teachings provided herein, a person of ordinaryskill in the art will know and appreciate other ways and/or methods toimplement embodiments of the present invention using hardware and acombination of hardware and software.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C++ or Perl using, for example, conventional or object-orientedtechniques. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission, suitable media include random access memory (RAM), a readonly memory (ROM), a magnetic medium such as a hard-drive or a floppydisk, or an optical medium such as a compact disk (CD) or DVD (digitalversatile disk), flash memory, and the like. The computer readablemedium may be any combination of such storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium according to an embodiment of the presentinvention may be created using a data signal encoded with such programs.Computer readable media encoded with the program code may be packagedwith a compatible device or provided separately from other devices(e.g., via Internet download). Any such computer readable medium mayreside on or within a single computer program product (e.g. a harddrive, a CD, or an entire computer system), and may be present on orwithin different computer program products within a system or network. Acomputer system may include a monitor, printer, or other suitabledisplay for providing any of the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including a processor, which can beconfigured to perform the steps. Thus, embodiments can be directed tocomputer systems configured to perform the steps of any of the methodsdescribed herein, potentially with different components performing arespective steps or a respective group of steps. Although presented asnumbered steps, steps of methods herein can be performed at a same timeor in a different order. Additionally, portions of these steps may beused with portions of other steps from other methods. Also, all orportions of a step may be optional. Additionally, any of the steps ofany of the methods can be performed with modules, circuits, or othermeans for performing these steps.

The specific details of particular embodiments may be combined in anysuitable manner without departing from the spirit and scope ofembodiments of the invention. However, other embodiments of theinvention may be directed to specific embodiments relating to eachindividual aspect, or specific combinations of these individual aspects.

The above description of exemplary embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdescribed, and many modifications and variations are possible in lightof the teaching above. The embodiments were chosen and described inorder to best explain the principles of the invention and its practicalapplications to thereby enable others skilled in the art to best utilizethe invention in various embodiments and with various modifications asare suited to the particular use contemplated.

A recitation of “a”, “an” or “the” is intended to mean “one or more”unless specifically indicated to the contrary. All patents, patentapplications, publications, and descriptions mentioned above are hereinincorporated by reference in their entirety for all purposes. None isadmitted to be prior art.

What is claimed is:
 1. A method of analyzing a biological sample of anorganism for chromosomal deletions or amplifications associated withcancer, the biological sample including nucleic acid moleculesoriginating from normal cells and potentially from cells associated withcancer, wherein at least some of the nucleic acid molecules arecell-free in the biological sample, the method comprising: determiningfirst and second haplotypes for normal cells of the organism at a firstchromosomal region, the first chromosomal region including a firstplurality of loci, wherein the first and second haplotypes areheterozygous at each of the first plurality of loci; for each of aplurality of the nucleic acid molecules in the biological sample:identifying a location of the nucleic acid molecule in a referencegenome of the organism; and determining a respective allele of thenucleic acid molecule; identifying a first group of nucleic acidmolecules as being from the first haplotype based on the identifiedlocations and determined alleles, the first group including at least onenucleic acid molecule located at each of the first plurality of loci;identifying a second group of nucleic acid molecules as being from thesecond haplotype based on the identified locations and determinedalleles, the second group including at least one nucleic acid moleculelocated at each of the first plurality of loci; calculating, with acomputer system, a first value of the first group of nucleic acidmolecules, the first value defining a property of the nucleic acidmolecules of the first group; calculating, with the computer system, asecond value of the second group of nucleic acid molecules, the secondvalue defining a property of the nucleic acid molecules of the secondgroup; and comparing the first value to the second value to determine aclassification of whether the first chromosomal region exhibits adeletion or an amplification in any cells associated with cancer,wherein the comparison includes determining a difference or a ratiobetween the first value and the second value, and wherein theclassification includes comparing the difference or the ratio to atleast one threshold value.
 2. The method of claim 1, wherein the firstchromosomal region is classified as exhibiting a deletion or anamplification in cells associated with cancer, and wherein the cellsassociated with cancer include a malignant tumor and/or from apremalignant lesion.
 3. The method of claim 1, wherein each locus of thefirst plurality of loci is at least 500 bases apart from another locusof the first plurality of loci.
 4. The method of claim 1, wherein the atleast one threshold value is derived from a healthy organism or from aregion not having a deletion or amplification, wherein the sequentialprobability ratio testing, the t-test, or the chi-square test is used todetermine the at least one threshold value.
 5. The method of claim 1,wherein the first value corresponds to a statistical value of a sizedistribution of the nucleic acid molecules of the first group, and thesecond value corresponds to a statistical value of a size distributionof the nucleic acid molecules of the second group.
 6. The method ofclaim 5, wherein the first value is an average size of the nucleic acidmolecules of the first group, and the second value is an average size ofthe nucleic acid molecules of the second group.
 7. The method of claim5, wherein the first value Q_(HapI) is a fraction of nucleic acidmolecules in the first group that are shorter than a cutoff size, andthe second value Q_(HapII) is a fraction of nucleic acid molecules inthe second group that are shorter than the cutoff size.
 8. The method ofclaim 7, wherein the comparison includes determining the differencebetween the first value and the second value, wherein the difference isΔQ=Q_(HapI−Q) _(HapII), and wherein a positive value of ΔQ greater thana threshold value indicates that the second haplotype includes adeletion in tumor tissue in the organism or that the first haplotypeincludes an amplification in tumor tissue in the organism.
 9. The methodof claim 7, wherein the comparison includes determining the differencebetween the first value and the second value, wherein the difference isΔQ=Q_(HapI)−Q_(HapII), and wherein an approximate value of zero for ΔQindicates that no deletion or amplification exists in the firstchromosomal region.
 10. The method of claim 5, wherein the first valueF_(HapI) and the second value F_(HapII) are defined for a respectivehaplotype as F=Σ^(w)length/Σ^(N)length, where Σ^(w)length represents asum of lengths of the nucleic acid molecules of the corresponding groupwith a length equal to or less than a cutoff size w; and Σ^(N)lengthrepresents a sum of lengths of the nucleic acid molecules of thecorresponding group with a length equal to or less than N bases, where Nis greater than w.
 11. The method of claim 10, wherein the comparisonincludes determining the difference between the first value and thesecond value, and wherein the difference is ΔF=F_(Hap I)−F_(Hap II), andwherein a positive value of ΔF greater than a threshold value indicatesthat the second haplotype includes a deletion in tumor tissue in theorganism.
 12. The method of claim 10, wherein the comparison includesdetermining the difference between the first value and the second value,and wherein the difference is ΔF=F_(Hap I)−F_(Hap II), and wherein anegative value of ΔF greater than a threshold value indicates that thesecond haplotype includes an amplification in tumor tissue in theorganism.
 13. The method of claim 1, wherein the first value of thefirst group corresponds to a number of nucleic acid molecules located atthe first plurality of loci, and the second value of the second groupcorresponds to a number of nucleic acid molecules located at the firstplurality of loci.
 14. The method of claim 13, further comprising:calculating the ratio of the first value and the second value todetermine a fractional concentration of cancer DNA in the biologicalsample.
 15. The method of claim 14, further comprising: determining thefractional concentration of cancer DNA in the biological sample at aplurality of times; and using the fractional concentration at theplurality of times to diagnose, stage, prognosticate, or monitorprogress of a level of cancer in the organism.
 16. The method of claim1, further comprising: for each of a plurality of other chromosomalregions: repeating the method of claim 1; determining a first number ofchromosomal regions that exhibit a deletion or an amplification; andcomparing the first number to one or more threshold values to determinea level of cancer in the organism.
 17. The method of claim 16, furthercomprising: determining an amount of deletion or amplification for eachchromosomal region that was identified as exhibiting a deletion or anamplification; and comparing the amount to one or more threshold valuesto determine the level of cancer in the organism.
 18. The method ofclaim 16, further comprising: repeating the method of claim 16 at aplurality of times; and using the first number at the plurality of timesto diagnose, stage, prognosticate, or monitor progress of the level ofcancer in the organism.
 19. The method of claim 18, wherein using thefirst number at the plurality of times to diagnose, stage,prognosticate, or monitor progress of the level of cancer in theorganism includes determining the presence or progress of a premalignantcondition.
 20. The method of claim 16, wherein each of the chromosomalregions are of predetermined length.
 21. A computer program productcomprising a computer readable medium storing a plurality ofinstructions for controlling a processor to perform an operation foranalyzing a biological sample of an organism for chromosomal deletionsor amplifications associated with cancer, the operation comprising:determining first and second haplotypes for normal cells of the organismat a first chromosomal region, the first chromosomal region including afirst plurality of loci, wherein the first and second haplotypes areheterozygous at each of the first plurality of loci; for each of aplurality of the nucleic acid molecules in the biological sample:identifying a location of the nucleic acid molecule in a referencegenome of the organism; and determining a respective allele of thenucleic acid molecule; identifying a first group of nucleic acidmolecules as being from the first haplotype based on the identifiedlocations and determined alleles, the first group including at least onenucleic acid molecule located at each of the first plurality of loci;identifying a second group of nucleic acid molecules as being from thesecond haplotype based on the identified locations and determinedalleles, the second group including at least one nucleic acid moleculelocated at each of the first plurality of loci; calculating a firstvalue of the first group of nucleic acid molecules, the first valuedefining a property of the nucleic acid molecules of the first group;calculating a second value of the second group of nucleic acidmolecules, the second value defining a property of the nucleic acidmolecules of the second group; and comparing the first value to thesecond value to determine a classification of whether the firstchromosomal region exhibits a deletion or an amplification in any cellsassociated with cancer, wherein the comparison includes determining adifference or a ratio between the first value and the second value, andwherein the classification includes comparing the difference or theratio to at least one threshold value.
 22. A computer system foranalyzing a biological sample of an organism for chromosomal deletionsor amplifications associated with cancer, the computer systemcomprising: one or more processors configured to implement the method ofclaim
 1. 23. The computer program product of claim 21, wherein the firstchromosomal region is classified as exhibiting a deletion or anamplification in cells associated with cancer, and wherein the cellsassociated with cancer include a malignant tumor and/or from apremalignant lesion.
 24. The computer program product of claim 21,wherein each locus of the first plurality of loci is at least 500 basesapart from another locus of the first plurality of loci.
 25. Thecomputer program product of claim 21, wherein the at least one thresholdvalue is derived from a healthy organism or from a region not having adeletion or amplification, wherein the sequential probability ratiotesting, the t-test, or the chi-square test is used to determine the atleast one threshold value.
 26. The computer program product of claim 21,wherein the first value corresponds to a statistical value of a sizedistribution of the nucleic acid molecules of the first group, and thesecond value corresponds to a statistical value of a size distributionof the nucleic acid molecules of the second group.
 27. The computerprogram product of claim 26, wherein the first value is an average sizeof the nucleic acid molecules of the first group, and the second valueis an average size of the nucleic acid molecules of the second group.28. The computer program product of claim 26, wherein the first valueQ_(HapI) is a fraction of nucleic acid molecules in the first group thatare shorter than a cutoff size, and the second value Q_(HapII) is afraction of nucleic acid molecules in the second group that are shorterthan the cutoff size.
 29. The computer program product of claim 28,wherein the comparison includes determining the difference between thefirst value and the second value, wherein the difference isΔQ=Q_(HapI)−Q_(HapII), and wherein a positive value of ΔQ greater than athreshold value indicates that the second haplotype includes a deletionin tumor tissue in the organism or that the first haplotype includes anamplification in tumor tissue in the organism.
 30. The computer programproduct of claim 28, wherein the comparison includes determining thedifference between the first value and the second value, wherein thedifference is ΔQ=Q_(HapI)−Q_(HapII), and wherein an approximate value ofzero for ΔQ indicates that no deletion or amplification exists in thefirst chromosomal region.
 31. The computer program product of claim 26,wherein the first value F_(Hap I) and the second value F_(Hap II) aredefined for a respective haplotype as F=Σ^(w)length/Σ^(N)length, whereΣ^(w)length represents a sum of lengths of the nucleic acid molecules ofthe corresponding group with a length equal to or less than a cutoffsize w; and Σ^(N)length represents a sum of lengths of the nucleic acidmolecules of the corresponding group with a length equal to or less thanN bases, where N is greater than w.
 32. The computer program product ofclaim 31, wherein the comparison includes determining the differencebetween the first value and the second value, and wherein the differenceis ΔF=F_(Hap I)−F_(Hap II), and wherein a positive value of ΔF greaterthan a threshold value indicates that the second haplotype includes adeletion in tumor tissue in the organism.
 33. The computer programproduct of claim 31, wherein the comparison includes determining thedifference between the first value and the second value, and wherein thedifference is ΔF=F_(Hap I)−F_(Hap II), and wherein a negative value ofΔF greater than a threshold value indicates that the second haplotypeincludes an amplification in tumor tissue in the organism.
 34. Thecomputer program product of claim 21, wherein the first value of thefirst group corresponds to a number of nucleic acid molecules located atthe first plurality of loci, and the second value of the second groupcorresponds to a number of nucleic acid molecules located at the firstplurality of loci.
 35. The computer program product of claim 34, whereinthe operation further comprises: calculating the ratio of the firstvalue and the second value to determine a fractional concentration ofcancer DNA in the biological sample.
 36. The computer program product ofclaim 35, wherein the operation further comprises: determining thefractional concentration of cancer DNA in the biological sample at aplurality of times; and using the fractional concentration at theplurality of times to diagnose, stage, prognosticate, or monitorprogress of a level of cancer in the organism.
 37. The computer programproduct of claim 21, wherein the operation further comprises: for eachof a plurality of other chromosomal regions: determining aclassification of whether the other chromosomal region exhibits adeletion or an amplification; determining a first number of chromosomalregions that exhibit a deletion or an amplification; and comparing thefirst number to one or more threshold values to determine a level ofcancer in the organism.
 38. The computer program product of claim 37,wherein the operation further comprises: determining an amount ofdeletion or amplification for each chromosomal region that wasidentified as exhibiting a deletion or an amplification; and comparingthe amount to one or more threshold values to determine the level ofcancer in the organism.
 39. The computer program product of claim 37,wherein the operation further comprises: determining the first number ofchromosomal regions that exhibit a deletion or an amplification at aplurality of times; and using the first number at the plurality of timesto diagnose, stage, prognosticate, or monitor progress of the level ofcancer in the organism.
 40. The computer program product of claim 39,wherein using the first number at the plurality of times to diagnose,stage, prognosticate, or monitor progress of the level of cancer in theorganism includes determining the presence or progress of a premalignantcondition.
 41. The computer program product of claim 37, wherein each ofthe chromosomal regions are of predetermined length.